The 18F-AV-1451 radioligand enables in-vivo identification of tau neurofibrillary tangles that are considered as biomarkers of neurodegeneration in Alzheimer Disease (AD). However, off-target radioligand binding is also observed in basal ganglia, known as an iron-rich region. Hence, it is important to distinguish between radioligand-identified tissue neurodegeneration and iron-related radioligand binding effects. Our aim is to answer this question by using previously developed quantitative Gradient Recalled Echo (qGRE) MRI technique sensitive to morphological and microstructural tissue neurodegeneration, as well as to iron deposition. Sixty-eight participants (ages: 72.1y ± 8.2y; 15 with amyloid status (Aβ) positive and cognitive dementia rating (CDR®)>0; 16 with Aβ-positive and CDR®=0; and 37 with Aβ-negative and CDR®=0) recruited by Knight ADRC underwent 18F-AV-1451 PET imaging and qGRE MRI imaging (3T Siemens MRI scanners). As previously demonstrated, the R2t* metric of qGRE signal is sensitive to both, pre-atrophic neuronal loss (low R2t*, a.k.a. Dark Matter (DM), and nonheme iron accumulation (High R2t*). The histogram characterizing distribution of R2t* values across the brain (Figure 1) allows the quantification of DM (R2t*<5.8s -1 ) and Iron Deposition Score (IDS, R2t*>28s -1 ). 18F-AV-1451 binding was characterized in terms of Standardized Uptake Value Ratio (SUVR). All results were summarized in brain regions segmented using FreeSurfer. (Figures 2 and 3): Tissue volumes exhibited moderate negative associations with 18 F-AV-1451 SUVRs in MTL (R 2 =0.21), temporal lobe (R 2 =0.17), and parietal lobe (R 2 =0.11). The linear model that includes IDS and DM exhibited positive association with 18 F-AV-1451 SUVRs only in MTL (R 2 =0.18). The linear mixed model that includes volume, IDS and DM metrics showed the improved association between predicted and actual 18F-AV-1451 tracer retention in the MTL (R 2 =0.34), the temporal lobe (R 2 =0.23), and the parietal lobe (R 2 =0.12). Our results suggest that the 18 F-AV-1451 binding is modulated not only by neurodegeneration but also by elevated levels of iron deposition. More detail studies are required to answer the question whether the iron effect is concomitant or contributes to neurodegeneration through ferroptosis. The qGRE-based multiparametric approach allowing simultaneous identification of pre-atrophic neurodegeneration and iron deposition has a potential to contribute to understanding of this intricate relationship.

Background: Atopic Dermatitis (AD) is a persistent skin condition that afflicts roughly 7% of adults in the United States(1). Emerging research underscores the intricate interplay between the human microbiome, the immune system, and their impact on the severity of AD. While previous reviews have concentrated on interventions targeting the gut microbiome, this systematic review represents a pioneering effort to evaluate the effectiveness of topical probiotic treatments for AD in both pediatric and adult populations.

Atopic dermatitis (AD) is a chronic skin disease that commonly appears during childhood but can present at any age. There are interventions that are effective in treating AD in children, but it has been difficult to find effective treatments for adults. This systematic review and meta-analysis sought to determine the efficacy of topical probiotic treatment for AD in adult populations. A database search was conducted of peer-reviewed, double-blind clinical trials, and studies underwent a systematic exclusion and inclusion process, yielding four that met the criteria. Disease severity, as measured by a standardized scoring tool SCORAD (SCORing Atopic Dermatitis), was culled from each study and compared to placebo at two-week and four-week time points. All studies showed improvement in SCORAD in the treatment groups compared to baseline at all time points. Two showed significant decreases in SCORAD after two weeks of treatment, and three studies showed long-lasting improvement after four weeks of treatment. Interestingly, while each study showed a reduction in the severity of AD at the two- and four-week time points, a pooled meta-analysis did not show a statistically significant difference between treatment and control at four weeks of treatment. Clinically, there may be benefits to topical probiotic usage as evidenced by the individual studies; more studies need to be performed including adults to show statistical significance.

PURPOSE/OBJECTIVES The objective of this study was to evaluate the effectiveness of an Electronic-Periodontal-Diagnosis-Tool (EPDT) to facilitate the formulation of a correct periodontal diagnosis and analyze students' perceptions of the use of the EPDT. METHODS Fifty Year-3 students who recently started their clinical training, were randomly assigned to two groups. Two clinical scenarios involving challenging periodontal diagnoses, each one with unique components, variables, and categories were distributed with specific instructions. The cases were analyzed to determine the correct periodontal diagnosis-half without the use of the EPDT and half with the use of the EPDT. A post-exercise discussion conducted by the faculty explained the answer rationales. The students completed an anonymous/voluntary survey to evaluate their perceptions. Statistical analysis using likelihood ratio chi-square tests and a generalized linear model evaluated whether the use of the EPDT resulted in higher percentages of correct diagnoses. RESULTS EPDT use resulted in a three times higher percentage of correct classifications than no tool use (48% versus 16%), which the investigators considered an important effect of the EPDT. The generalized-linear-model assessment confirmed that EPDT resulted in better classifications (p < 0.0001). The feedback about the perceptions of the EPDT was favorable. CONCLUSION Students using the EPDT resulted in higher percentages of correct diagnoses. The EPDT provided students with a useful framework to determine the correct periodontal diagnoses, which is essential in providing appropriate treatments.

OBJECTIVES:The aim of this literature review was to summarise the clinical important findings on the endodontic treatment outcome in older patients (≥60 years old) with pulpal/periapical disease considering local and systemic factors from a body of knowledge that is heterogeneous in methods or disciplines.BACKGROUND:Due to the increasing number of older patients in the endodontic practice, and the current trend for tooth preservation, the need for clinicians to have a better understanding of age-related implications that may influence the required endodontic treatment in older adults to retain their natural dentition is indispensable.METHODS:PubMed/Medline and Embase was searched by a medical librarian using specific terms based on inclusion/exclusion criteria. The reference list was hand-seached for additional relevant publications between 2005-2020. A combination of these terms was performed uing Boolean operators and MeSH terms.RESULTS:Of the 1577 publications identified manually and electronically, 25 were included to be fully reviewed by the examiners. The data was derived from three systematic reviews, one systematic and meta-analysis, three case series, four prospective and 14 retrospective cohorts. Overall, there was heterogeneity in reporting as well as limitations in most studies.CONCLUSIONS:The outcome of endodontic treatment (ET) either nonsurgical or surgical or combination of these is not affected by older age. ET can be the treatment of choice in older patients wiht pulpal/periapical disease. There is no evidence that older age per se affects the outcome of any type of endodontic treatment.

An editorial published earlier this year considered the importance of the significance of differences for results in biomedical research, especially in oral and maxillofacial radiology, with emphasis on how these analyses can be misused or misinterpreted.1Hildebolt CF The use and presentation of statistical assessments in oral and maxillofacial radiology research: red flags and essentials. Part 1.Oral Surg Oral Med Oral Pathol Oral Radiol. 2023; 135: 157-160Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar In this earlier editorial, various topics were reviewed, including (a) the concept of P values and the distinction between statistically significant and clinically important differences, (b) the process of selecting participants who are representative of the affected population and are independent of each other (note that multiple measurements of a person/animal/item are not considered to be independent measurements), (c) the importance of properly selecting tests for quantitative vs categorical data, (d) the determination of normal vs non-normal distributions for analysis of quantitative data that is essential in choosing tests that produce meaningful comparisons, and (e) the selection of tests that can be applied for multiple measurements of the same sample. In this second editorial, additional parameters of statistical analysis, and the ways they are omitted or misused, are discussed, including alpha, beta, power calculations, power adjustments for multiple tests, Youden's J statistic, and determination of the repeatability of measurements. Although the P value expresses the probability of obtaining an effect greater than or equal to the effect observed with the reference data, the alpha (α) level is the probability of claiming a difference where none exists (i.e., a false-positive or Type I error). The probability of making this error is equal to α, and the probability of not making an error is equal to (1 – α). The probability of not making an error in m tests equals (1 α)m, and the probability of making 1 or more errors in m tests equals 1 − (1 − α)m. If 2 tests were performed with α = 0.05, there would be a 10% chance of a false-positive result: 1 – (0.95)2 = 1 – 0.90 = 10%. If an oral and maxillofacial radiology researcher performs an analysis of variance to assess whether there is variation in an outcome measurement (Y variable) within and between 5 groups of patients (X [predictor variables]), this will result in 1 overall P value for the within-group variation and 10 P values for 10 t tests to assess between-group variations. Assuming that all required assumptions are fulfilled, the researcher should report only the overall P value if α = 0.05 and the overall within-group P value is ≥.05. However, if overall P < .05, all 10 between-group P values should be reported along with their corresponding mean values and 95% CIs. Reporting all 10 P values, however, can be a problem. For the above 5-group comparison, there would be a 40% chance of a false-positive result (1 – [0.95]10 = 1 – 0.60 = 40%); that is, 4 of the 10 tests would result in false-positive results, but which ones? To avoid false-positive results, P values can be adjusted for multiple comparisons with a Bonferroni correction, which is performed by dividing α by the number of comparisons that will be made. With α = 0.05, the Bonferroni-corrected α value for 3 comparisons is 0.0167 (0.05/3). If all 3 tests are performed, the P value for each test is essentially 0.05 (i.e., 0.0167 × 3 = 0.05). Such a correction for 10 comparisons (0.05/10) results in an adjusted α value of 0.005. There are many methods that adjust P values for multiple comparisons, and most are less conservative than the Bonferroni technique, including the Tukey and Tukey-Kramer honestly significant difference tests, the Student-Newman-Keuls and Scheffe tests, and the false discovery rate calculator.2Benjamini Y Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testing.J R Stat Soc Ser B. 1995; 57: 289-300Google Scholar If multiple tests are performed in a study, the false discovery rate calculator is a powerful approach to control the risk of false-positive errors. However, large numbers of comparisons are not recommended; this suggests poorly specified research objectives.3Altman DG Practical Statistics for Medical Research. Chapman & Hall/CRC, New York1999Google Scholar I recently submitted a review of a manuscript to the editor of another journal. In total, 39 P values were given without any adjustment for the multiple P values. Last year, I reviewed a manuscript submitted to another top-rated dental journal. In the manuscript, 101 P values were presented, and the words “significant” or “significantly” were used 27 times. To me, as indicated in the earlier editorial, the overuse of P values and the words “significant” or “significantly” is unacceptable for a manuscript to be published in a scientific journal. In addition, it is not appropriate for an investigator to split a data set into multiple subsets, perform multiple null hypothesis tests, and report the test with the lowest P value. Another way to avoid false rejection of the null hypothesis (a false-positive result) would be to replicate the study under different circumstances but with the same protocol. The US Food and Drug Administration usually requires 2 independent clinical trials with an error rate of 0.05 to approve a new pharmaceutical. If both trials result in P < .05, the probability of falsely rejecting the null hypothesis of no beneficial effect of a new pharmaceutical is <0.0025; that is, 1/0.0025 = 1 in 400 chance of false rejection. It has been suggested that a more conservative P value (< .005) be used to discover a new effect.4Trafimow D Marks M Editorial in basic and applied social pschology.Basic Appl Social Pschol. 2015; 37: 1-2Crossref Scopus (483) Google Scholar P < .01 and < .001 could also be used and are considered “strong evidence” and “very strong evidence” of a difference or relationship.5Amrhein V Greenland S McShane B Scientists rise up against statistical significance.Nature. 2019; 567: 305-307Crossref PubMed Scopus (1619) Google Scholar In addition to the type I error rate (α), the type II error rate (beta [β]) is the probability of claiming no difference when a difference truly exists (a false-negative decision). The β level represents the ability to reject the null hypothesis, thereby indicating that the test will be able to detect the difference. There is a reciprocal relationship between α and β, and 1 – β equals the power of the test, which is the probability that the test will be valid in detecting the existence of an actual difference.6SASJmp 13 Fitting Linear Models. Second Edition. SAS Institute, Cary, NC2017: 139Google Scholar As part of the review process of a manuscript that reports statistical results, some journals require a power analysis. Some funding agencies suggest power values of 80% to be eligible for grant support. Power calculations can be performed with most statistical programs. Investigators should calculate power and adjust P values for multiple comparisons. Reviewers and readers cannot properly assess the presented results without such calculations and adjustments. The Youden J statistic7Youden WJ Index for rating diagnostic tests.Cancer. 1950; 3: 32-35Crossref PubMed Scopus (7961) Google Scholar is sometimes used to interpret the area under the curve in receiver operating characteristic (ROC) analysis.8Schoonjans F. MedCalc Manual Easy-to-Use Statistical Software. MedCalc Software, Ostend, Begium2019Google Scholar Most statistical software can create an ROC curve and calculate the Youden J. MedCalc's software manual provides explanations for ROC analysis and the Youden J statistic.8Schoonjans F. MedCalc Manual Easy-to-Use Statistical Software. MedCalc Software, Ostend, Begium2019Google Scholar The J value is defined as (sensitivity + specificity) – 1. If sensitivity and specificity are each 1.0 (no false positives or false negatives), the J statistic is 1, indicating a perfect test. If each of these measurements is no greater than 0.5, representing random chance, the J statistic is zero, and the test is worthless. J statistics are calculated for all input values used to create an ROC curve, with the highest J value indicating the optimal input value for sensitivity on the y-axis and 1 − specificity (false-positive fraction) on the x-axis. This value is the point at which sensitivity has the highest value, and the false-positive fraction has the lowest value. As part of this determination, it is assumed that sensitivity and specificity are equally important; however, this assumption may not always be appropriate. If researchers are assessing a serious disease or condition, inputs that result in the highest sensitivity values are of the utmost importance because it is crucial that no true case be missed. However, if false positives could result in high patient costs and risks, inputs that result in the lowest 1 − specificity (false-positive fraction) value would be more important. Investigators must carefully assess the information represented by ROC analysis and present this information in the manuscript. Researchers can also use statistical software to test for differences between areas under the curves, which can be useful information if a study performs 2 or more ROC analyses. As mentioned in the earlier editorial, assessing a test's repeatability is essential; this is especially true when diagnostic and/or therapeutic decisions are made. A clinician needs to know how large the difference between baseline and follow-up measurements should be before a test can identify a true biologic change and not just noise, that is, equipment and/or observer error.9Bonnick SL Bone Densitometry in Clinical Practice: Application and Interpretation. Humana Press, Totowa, NJ2003Crossref Google Scholar If repeated measurements of the same subjects vary considerably, agreement between methods will be bad, and the study will not be repeatable.10Bland JM Altman DG Measuring agreement in method comparison studies.Stat Methods Med Res. 1999; 8: 135-160Crossref PubMed Scopus (7075) Google Scholar Each method to be compared should be measured twice on each subject so that agreement between methods and repeatability of each method can be determined.10Bland JM Altman DG Measuring agreement in method comparison studies.Stat Methods Med Res. 1999; 8: 135-160Crossref PubMed Scopus (7075) Google Scholar,11Bland JM Altman DG Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. 1986; 1: 307-310Abstract PubMed Scopus (40620) Google Scholar In a 2003 article, Bland and Altman stated, “Many research papers in imaging concern measurement. This is a topic that in the past has been much neglected in the medical research methods literature.”12Bland JM Altman DG Applying the right statistics: analyses of measurement studies.Ultrasound Obstet Gynecol. 2003; 22: 85-93Crossref PubMed Scopus (1121) Google Scholar The authors illustrated proper measurement assessment methods with examples drawn from imaging literature. It is exceedingly important that imaging studies use proper methods. In 2007, Bland and Altman introduced methods for applying multiple measurements per subject.13Bland JM Altman DG Agreement between methods of measurement with multiple observations per individual.J Biopharm Stat. 2007; 17: 571-582Crossref PubMed Scopus (1344) Google Scholar One of the authors (Altman) was a coauthor for early versions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement.14Liberati A Altman DG Tetzlaff J et al.The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration.BMJ. 2009; 339: b2700Crossref PubMed Scopus (12459) Google Scholar In determining the repeatability of measurements, the mean difference should be zero; otherwise, knowledge of the original measurement influences the repeat measurement, or the measurements are affected by the measurement process.12Bland JM Altman DG Applying the right statistics: analyses of measurement studies.Ultrasound Obstet Gynecol. 2003; 22: 85-93Crossref PubMed Scopus (1121) Google Scholar When differences between 2 measurement methods are plotted against means, values for 1 method may consistently exceed values for the other. This difference is known as “bias,” and it can be corrected by adding the bias to the values of the new method.11Bland JM Altman DG Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. 1986; 1: 307-310Abstract PubMed Scopus (40620) Google Scholar If the mean differences between measurements are normally distributed, differences can be assessed with paired t tests or 95% CIs to determine whether 0 is within the CIs. However, the difference should be 0, with the focus being on the limits of agreement (and their 95% CIs) to assess the distribution of differences. These authors use the repeatability coefficient, as per the British Standards Institution,15British Standards InstitutionPrecision of Test Methods 1: Guide for the Determination and Reproducibility for a Standard Test Method (BS 597, Part 1). BSI, London1975Google Scholar to indicate how large a change in measurement must be to have 95% confidence that the change is real and not caused by measurement error. The values for the repeatability coefficient are nearly identical to those determined by the International Society for Clinical Densitometry for the least significant change, which is the magnitude of change needed to indicate with 95% confidence that a true biologic alteration has occurred, for a dual-energy X-ray absorptiometry assessment.9Bonnick SL Bone Densitometry in Clinical Practice: Application and Interpretation. Humana Press, Totowa, NJ2003Crossref Google Scholar It is exceedingly important that the 95% CIs be calculated for the limits of agreement, and the standard error is used to do this.10Bland JM Altman DG Measuring agreement in method comparison studies.Stat Methods Med Res. 1999; 8: 135-160Crossref PubMed Scopus (7075) Google Scholar,11Bland JM Altman DG Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. 1986; 1: 307-310Abstract PubMed Scopus (40620) Google Scholar Confidence intervals represent multiple samples from the population and should be reported. Confidence intervals can be used in determining the sample size required to achieve a stated power.16Lu MJ Zhong WH Liu YX Miao HZ Li YC Ji MH Sample size for assessing agreement between two methods of measurement by Bland-Altman method.Int J Biostat. 2016; 12 (/j/ijb.2016.12.issue-2/ijb-2015-0039/ijb-2015-0039.xml)Crossref PubMed Scopus (153) Google Scholar Oral and maxillofacial radiology researchers must keep in mind that “bad statistics leads to bad research and bad research is unethical,”17Bland M An Introduction to Medical Statistics. Oxford University Press, Oxford, UK2000Google Scholar and “statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty; and it thereby provides the navigation essential for controlling the course of scientific and societal advances.”18Davidian M Louis TA Why statistics?.Science. 2012; 336: 12Crossref PubMed Scopus (38) Google Scholar None.

Background and aims Probiotics are widely used and prescribed to address a host of health issues. Despite evidence that different probiotic bacteria have differing therapeutic mechanisms of action, many probiotics are prescribed indiscriminately, with little research to support the use of specific formulations for a given ailment. Further investigation is required to assess the efficacy of one commonly prescribed probiotic formulation Lactobacillus acidophilus and Lactobacillus bulgaricus (helveticus) – for the treatment of diarrhea. This review seeks to assess whether administration of probiotics composed of L.acidophilus and L. bulgaricus (helveticus) are more effective than placebo in reducing symptoms of diarrhea. Methods A systematic search of randomized placebo-controlled trials evaluating the effectiveness of combination L. acidophilus and L. bulgaricus in the treatment of diarrhea by any cause was conducted and captured all available studies (n = 2411). After application of exclusion criteria, four studies were identified as suitable for inclusion. Separate meta-analyses were conducted for the proportion of cases with diarrhea in the placebo group and the treatment group. To assess differences in proportions between the placebo and treatment groups, a generalized linear model assessment was performed. Results Analyses revealed the overall proportion of cases with diarrhea in the treatment group, 36 participants who had diarrhea out of 91 total, was only 3.5% lower than the overall proportion in the placebo group, 44 participants who had diarrhea out of 105 total.(P = 0.508), with our considering that the 3.5 lower percentage to be of little or no clinical importance. Conclusion Existing literature suggests little or no clinical benefit of a L. acidophilus and L. bulgaricus probiotic formulation for the treatment of diarrhea, highlighting the need for more research or re-evaluation of its widespread use.

Introduction: Infection control compliance in dental schools has been reported as less than ideal and requires improvement. The goal of our study was to evaluate the effectiveness of a centralized educational strategy that used multimedia to improve subject understanding and compliance with infection control guidelines and practices.Materials and Methods: The training strategy was created to show clinical scenarios and to outline all information relevant to using proper infection control and safety procedures. Pre- and post-intervention observation scores were collected for 59 students, with the scores being used to assess proper use or handling of barriers, personal protective equipment, sharps, handwashing, and disinfection. Scores were summed to form a Total score that was assessed with the non-parametric Wilcoxon-test for paired samples.Results: For Total scores, 24 of the 59 students (41%) had higher post-video scores whereas only 14 students (24%) had higher pre-intervention scores (P = 0.04).Discussion: This study revealed overall improvements in the infection control practices after an educational intervention, especially for personal protective equipment with 15 positive differences and 6 negative differences, and hand washing scores with 26 positive differences and 16 negative differences.Conclusions: We consider the higher post-training scores to be clinically important and indicate that didactic intervention is effective in improving IC practices in the school clinic.

Statistics has advanced considerably since the term "statistical significance" became popular in the 1920s, when Ronald Fisher created threshold-value tables for P<0.05 and P<0.01.1Kennedy-Shaffer L. Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing.Am Stat. 2019; 73: 82-90https://doi.org/10.1080/00031305.2018.1537891Crossref PubMed Scopus (51) Google Scholar The popularity of these tables led to these thresholds becoming established conventions; however, there are problems associated with these arbitrary thresholds. "P values are a way of reporting the results of statistical tests, but they do not define the practical importance of the results."2Bailar JC Mosteller F. Medical Uses of Statistics.New England Journal of Medicine Books;. 1992; : 181Google Scholar In recent decades, P values in research have become rampant. A 3-year review of 18 psychology and neurology journal articles found 30,000 P values,3Szucs D Ioannidis JP. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature.PLoS Biol. 2017; 15e2000797https://doi.org/10.1371/journal.pbio.2000797Crossref PubMed Scopus (360) Google Scholar and in 2015, Basic and Applied Social Psychology announced that the journal would not publish papers containing P values because the statistics were often used to support lower-quality research.4Trafimow D Marks M. Editorial in basic and applied social pschology.Basic and Applied Social Pschology. 2015; 37: 1-2Crossref Scopus (458) Google Scholar The P value is an index of the strength of evidence against the null hypothesis; it does not indicate the clinical importance of findings. For example, suppose 3 patients take drug A, live long, and prosper; 3 other patients take drug B and die horrible deaths. Fisher's exact test indicates no difference between treatments (P=0.10); however, the clinical importance of the difference would be obvious. If there were 4 patients in each group, the P value would be 0.03, and it is highly unlikely that any clinician would have trouble interpreting this P value. To illustrate the depth of the problem, 51% (402/791) of articles from five journals erroneously interpreted statistically non-significant results as indicating "no effect".5Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305-307. http://doi.org/10.1038/d41586-019-00857-9Google Scholar An article written by statisticians and scientists in 2019 focused on the overuse of the words "significant/significantly".5Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567(7748):305-307. http://doi.org/10.1038/d41586-019-00857-9Google Scholar This paper had 800 signatories and many supporting articles. Among the recommendations, it was emphasized that the clinical importance of findings, not their statistical significance, should be stressed, and that statements like these should not be made:'There was (or was not) a significant difference between groups.'Instead, it would be better to say:'Our results are most compatible with an (or no) important effect.' A study should contain a full, clear disclosure of what was done, the results, how results were interpreted, and why.6Bailar J.C.MF Medical Uses of Statistics.New England Journal of Medicine Books. 1992; : 23Google Scholar It is also important to include a graphic plot or table in support of the statement of findings. There should be no ban on P values and statistical analyses if used properly. Statisticians have written, "Statistics, the science of assembling and interpreting numerical data, is the core science of evidence-based practice."7Bland M. An introduction to medical statistics.Oxford medical publications. 3rd ed. Oxford University Press, 2000: 1Google Scholar, and "Statistics is the science of learning from data, and of measuring, controlling, and communicating uncertainty; and it thereby provides the navigation essential for controlling the course of scientific and societal advances."8Davidian M Louis TA. Why statistics?.Science. 2012; 336: 12https://doi.org/10.1126/science.1218685Crossref PubMed Scopus (36) Google Scholar The term "clinically significant" is sometimes used for differences that are important clinically, but it must be made clear to readers what is meant by this term. Reviewers and readers may not agree with the authors' assertion concerning the clinical importance of their findings. Before the authors perform their study, however, they must decide what findings they would consider to be clinically important, and they should provide data in support of their assertion. Guidelines such as the 27-item PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement are helpful in planning and performing research.9Page MJ Moher D Bossuyt PM et al.PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews.BMJ. 2021; 372: n160https://doi.org/10.1136/bmj.n160Crossref PubMed Scopus (2044) Google Scholar It is good practice to use one of these statements, or "introductory-guidelines,"10Fowkes FG Fulton PM. Critical appraisal of published research: introductory guidelines.BMJ. 1991; 302: 1136-1140Crossref PubMed Scopus (277) Google Scholar one of which is that the sample must be representative of the study population, which must be representative of the target population (infinite-data case). Random sampling is best; the use of every-other subject or subjects attending a clinic may introduce referral bias. Authors must decide whether their conclusions are justified,11Albert DA. Deciding whether the conclusions of studies are justified: a review.Medical decision making: an international journal of the Society for Medical Decision Making. 1981; 1: 265-275https://doi.org/10.1177/0272989X8100100306Crossref PubMed Scopus (7) Google Scholar assess their study's risk of bias,12Higgins JP Altman DG Gotzsche PC et al.The Cochrane Collaboration's tool for assessing risk of bias in randomised trials.BMJ. 2011; 343: d5928https://doi.org/10.1136/bmj.d5928Crossref PubMed Scopus (19580) Google Scholar and remember that their investigation must be reproducible.13Nuzzo R. How scientists fool themselves - and how they can stop.Nature. 2015; 526: 182-185https://doi.org/10.1038/526182aCrossref PubMed Scopus (197) Google Scholar The US Food and Drug Administration (FDA) published a document in 2007 emphasizing that the term "reference standard" be used instead of "truth" or "gold standard," that sensitivity and specificity be reported, and that the assumption of independence be followed.14Department of Health and Human Services of the Food and Drug Administration (Center for Devices and Radiological Health DDB, Division of Biostatistics, Office of Surveillance and Biometrics).Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. 2007; (Accessed 7/27/2022)http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071148.htmGoogle Scholar Multiple measurements of a person/animal/item are not independent measurements. To treat each tooth/site in a person as an independent sample violates the assumption of independence required for many statistical tests. Numerous periodontal research articles have addressed the problems associated with using multiple sites as independent measurements.15Imrey PB. Considerations in the statistical analysis of clinical trials in periodontitis.J Clin Periodontol. 1986; 13: 517-532https://doi.org/10.1111/j.1600-051x.1986.tb01500.xCrossref PubMed Google Scholar, 16Imrey PB Chilton NW. Design and Analytic Concepts for Periodontal Clinical Trials.J Periodontol. 1992; 63: 1124-1140https://doi.org/10.1902/jop.1992.63.12s.1124Crossref Scopus (15) Google Scholar, 17Albandar JM Goldstein H. Multi-level statistical models in studies of periodontal diseases.J Periodontol. 1992; 63: 690-695https://doi.org/10.1902/jop.1992.63.8.690Crossref PubMed Scopus (45) Google Scholar In temporomandibular joint (TMJ) studies, investigators sometimes consider right and left TMJs to be independent—a "diseased" TMJ on one side is considered independent of the contralateral "normal" TMJ. But the TMJs are not independent of each other. If investigators perform an assessment in which they assume they are, justification for this potential violation of the assumption of independence must be provided. In the words of a well-known statistician, "The idea of independence is an essential statistical concept. By independence, we mean that if we know the outcome of one event this tells us nothing about the other event. More formally, the probability of each possible outcome for the second event is the same regardless of the outcome of the first event ...."18Altman DG. Practical statistics for medical research. Chapman & Hall/CRC, 1999: 49Google Scholar There are, however, methods for analyzing multiple measurements from the same persons/animals/items. For testing of 2 repeated, continuous measurements (with differences normally distributed), the parametric paired t-test can be used. Repeated-measures analysis of variance (ANOVA) could be used for 3 or more repeated measurements, and this analysis can include one or more between-group factors. However, this analysis requires assumptions of compound symmetry (homogeneous pooled within-group variances and across-subjects covariances) plus sphericity (orthogonal components). Repeated-measures multivariate analysis of variance (MANOVA) is therefore commonly used because it bypasses these assumptions.19Hill T Lewiki P. Statistics Methods and Applications.StatSoft. 2006; : 51-55Google Scholar Such an analysis is best performed with data that are complete and balanced. If not, a mixed model can be used. Mixed models are an extension of classic statistical models (such as ANOVA and regression) and are among the most powerful and useful methods for analyzing data.20Hummel R.M. Claassen E.A. Wolfinger R.D. JMP for Mixed Models. SAS Institute Inc., 2021Google Scholar Mixed models can contain one or more fixed effects, which are assumed to be constant in the experiment and within the population, and also contain one or more random effects with measurements considered to be randomly selected from the population. For example, a practitioner might want to assess whether the number of radiographs acquired of patients over a period of time was associated with variables such as the age and dental health of each patient. With age and dental health [X (predictor variables)] as fixed effects, patient [X (predictor variable)] as a random effect, and the number of radiographs acquired [(Y) variable] as the outcome, a mixed model could be used for this assessment. Mixed model assessments cannot be performed with some statistical software and can be difficult to conduct. If an investigator plans to use a mixed model and is not familiar with this method, it would be best to get help with these analyses. In addition, there are tests for assessing differences in samples of paired nominal and ordinal data, with these methods being described in most statistical textbooks and user guides for statistical software. Along with the assumption of independence, other assumptions are required for proper statistical assessments. "Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality must be checked for many statistical procedures, namely parametric tests, because their validity depends on it."21Ghasemi A Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians.Int J Endocrinol Metab. 2012; 10 (Spring): 486-489https://doi.org/10.5812/ijem.3505Crossref PubMed Scopus (1872) Google Scholar The equality of variances also needs to be tested for comparisons of group means, and to assure that required assumptions are not violated, the distributions of residuals should be assessed. Short descriptions of how residuals were assessed should be reported. Most statistical packages provide graphical methods for performing these assessments. One commonly used method goes by the names "normal quantile plot" or "quantile-quantile" plot ("Q-Q plot"). Moreover, parametric and nonparametric data require different statistical analyses, as do different data types (continuous numeric, nominal, ordinal, and counts). As stated above, the FDA requires sensitivity and specificity to be reported.14Department of Health and Human Services of the Food and Drug Administration (Center for Devices and Radiological Health DDB, Division of Biostatistics, Office of Surveillance and Biometrics).Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. 2007; (Accessed 7/27/2022)http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071148.htmGoogle Scholar These values should be given with 95% confidence intervals (CIs). Additional diagnostic-performance values (with 95% CIs) that often should be reported are true positives (TPs), false positives (FPs), true negatives (TNs), false negatives (FNs), and accuracy (TP+TN)/(TP+ FP+TN+FN). The presence or absence of a condition versus positive or negative assessment results is often presented in a 2 × 2 table (confusion matrix) to help readers assess diagnostic performance. In many diagnostic-performance articles, receiver operating characteristic (ROC) curves and/or areas under the curve (AUCs) are reported. An ROC curve is a plot for the diagnostic ability of a binary classifier as its discrimination threshold varies. For the curve, sensitivities are plotted against 1-specificities (false-positive fractions). An AUC of 0.91 equals the probability that a randomly chosen abnormal case is correctly rated or ranked with greater suspicion than a randomly chosen normal case. AUC should be presented with 95% CIs, and its statistical significance is often reported. Authors who present AUC values should interpret the meaning of the results. One commonly used interpretation regarding the discriminating ability of the values states that AUC of 0.5=none, 0.5-0.7=poor, 0.7-0.8=acceptable, 0.8-0.9=excellent, and >0.9=outstanding discrimination.22Hosmer DWJ Lemeshow S. Sturdivant R.X. Applied Logistic Regression, Third Edition. John Wiley & Sons, Inc., 2013: 177https://onlinelibrary.wiley.com/doi/book/10.1002/9781118548387Google Scholar Investigators must carefully consider ROC data and the sensitivities and false positive fractions that it represents. If they are assessing a serious disease or condition, inputs that result in the highest sensitivity values are of the utmost importance. However, if false positives could result in excessive costs and risks for patients, inputs that result in the lowest 1-specificity (false positive fraction) values would be more important. Positive and negative likelihood ratios (LRs) are sometimes reported for diagnostic performance. A positive LR is the ratio of a positive test given the presence of disease to a positive test given the absence of disease (TP/FP); bigger is better. A negative LR is the ratio of a negative test result given the presence of the disease to a negative test result given the absence of the disease (FN/TN); smaller is better. Authors should interpret the clinical importance of LRs. For example: 0.1=large decrease, 0.2=moderate decrease, 0.5=slight decrease, 1=neither decrease or increase, 2=slight increase, 5=moderate increase, and 10=large increase.23Henderson MCT, Lawrence M.; Smetana, Gerald W. The Patient History (2nd ed.). McGraw-Hill. Accessed 23 September 2022, https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testingGoogle Scholar When a reference standard is not available for categorical data, percent agreement should be reported.14Department of Health and Human Services of the Food and Drug Administration (Center for Devices and Radiological Health DDB, Division of Biostatistics, Office of Surveillance and Biometrics).Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. 2007; (Accessed 7/27/2022)http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071148.htmGoogle Scholar In these situations kappa is sometimes reported,24Landis JR Koch GG. The measurement of observer agreement for categorical data.Biometrics. 1977; 33: 159-174Crossref PubMed Scopus (52327) Google Scholar with kappa representing correction for chance agreement. If the data are ordinal, weighted kappa is used.25Altman DG. Practical statistics for medical research. Chapmen & Hall/CRC, 1999: 404-406Google Scholar Kappa values for agreement can be interpreted as follows: 0.00-0.20=poor, 0.21-0.40=fair, 0.41-0.60=moderate, 0..61-0.80=good, and 0.81-1.00=very good.26Altman DG. Practical statistics for medical research. Chapman & Hall/CRC, 1999: 404Google Scholar Positive predictive values [TP/(TP+FP)] and negative predictive values [TN/(TN+FN)] are often reported but differ from the other outcomes measures because they are dependent on disease prevalence in the population, which may not equal the prevalence in the sample.27Hoffrage U Lindsey S Hertwig R Medicine Gigerenzer G. Communicating statistical information. Research Support, Non-U.S. Gov't.Science. 22 2000; 290: 2261-2262Crossref PubMed Scopus (428) Google Scholar Ignoring this can result in misleading predictive values. For example, ex-vivo research on dental caries imaging often uses samples with frequency of lesions much greater than caries rates in most populations. This can call into question the generalizability of the results. In 1983, methods were introduced for (1) comparing the agreement between 2 measurement methods and (2) determining the repeatability of measurements made with the same method.28Altman DG Bland JM. Measurement in medicine: the analysis of method comparison studies.Statistician. Jun 1983; 32: 307-317Crossref Google Scholar This article was written for statisticians, but clinicians became aware of the paper and requested that an article be written for them. In 1986, this was done,29Bland JM Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. Feb 8 1986; 1: 307-310Abstract PubMed Scopus (39779) Google Scholar and the paper has become exceedingly popular. The method, commonly known as the Bland-Altman method or technique, is practical and easy to understand. To determine agreement/repeatability, the differences between two measurements are plotted against the means of the measurements. As part of the plot, 95% limits of agreement are included, with 95% of the differences between measurements located within the 95% CIs. If the limits of agreement are clinically acceptable, the two sets of measurements are considered equivalent. What constitutes acceptable limits of agreement is not a statistical decision but a clinical decision and needs to be determined before the study is performed.30Bland JM Altman DG. Applying the right statistics: analyses of measurement studies.Ultrasound in obstetrics & gynecology: the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2003; 22: 85-93https://doi.org/10.1002/uog.122Crossref PubMed Scopus (1100) Google Scholar With this said, limits of agreement apply to the samples used. To extrapolate findings to the population of all measurements, it is necessary to calculate precision (the 95% CIs for the limits of agreement). If the upper and lower bounds of these confidence intervals are clinically acceptable, this is strong support for the sets of measurements being equivalent. Even if a P value for the differences in the means of measurements indicates a "statistically significant difference", keep in mind that the larger the sample size the smaller the P value, with the clinical importance of the limits of agreement being more important. Although regression analysis, correlation coefficients, and intraclass correlation coefficients are commonly and correctly used in many scientific studies, there are limitations associated with these tests when they are employed to assess agreement and repeatability. Most importantly, demonstrating association does not demonstrate agreement or repeatability unless the two methods have different units of measurement, in which case the mean of prediction intervals resulting from regression analyses needs to be used.29Bland JM Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement.Lancet. Feb 8 1986; 1: 307-310Abstract PubMed Scopus (39779) Google Scholar, 30Bland JM Altman DG. Applying the right statistics: analyses of measurement studies.Ultrasound in obstetrics & gynecology: the official journal of the International Society of Ultrasound in Obstetrics and Gynecology. 2003; 22: 85-93https://doi.org/10.1002/uog.122Crossref PubMed Scopus (1100) Google Scholar, 31Bland JM Altman DG. A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement.Comput Biol Med. 1990; 20: 337-340Crossref PubMed Scopus (532) Google Scholar In performing a study, therefore, researchers must ensure that: (1) their sample is representative of the target population, (2) correct statistical assessments are performed, (3) statistical assessments are correctly interpreted (with emphasis on the clinical importance, not the statistical significance, of findings), and (4) the article clearly explains what was done, the results, how results were interpreted, and why. Investigators need to (5) "Describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to verify the reported results."32Bailar JC Mosteller F. Medical Uses of Statistics.New England Journal of Medicine Books. 1992; : 314Google Scholar

Introduction: Probiotics are widely used and prescribed to address a host of health issues. Despite evidence that different probiotic bacteria have differing therapeutic mechanisms of action, many probiotics are prescribed indiscriminately, with little research to support the use of specific formulations for a given ailment. Further investigation is required to assess the efficacy of one commonly prescribed probiotic formulation – Lactinex (Lactobacillus acidophilus and Lactobacillus helveticus (bulgaricus)) – for the treatment of diarrhea. This review seeks to assess whether administration of probiotics containing Lactobacillus acidophilus and Lactobacillus helveticus (bulgaricus) are more effective than placebo in reducing symptoms of diarrhea. Methods: A systematic search of randomized placebo-controlled trials evaluating the effectiveness of combination Lactobacillus acidophilus and Lactobacillus bulgaricus in the treatment of diarrhea by any cause was conducted and captured all available studies (n = 2411). After application of exclusion criteria, four studies were identified as suitable for inclusion. Separate meta-analyses were conducted for the proportion of cases with diarrhea in the control group and the treatment group. To assess differences in proportions between the control and treatment groups, a generalized linear model assessment was performed. Results: Analyses revealed the overall proportion of cases with diarrhea in the treatment group was only 3.5% lower than the overall proportion in the control group (P = 0.508), with our considering that the 3.5 lower percentage to be of little or no clinical importance. Conclusion: Existing literature suggests little or no clinical benefit of Lactinex for the treatment of diarrhea, highlighting the need for more research or re-evaluation of its widespread use.Figure 1.: Shows the effectiveness of Lactinex vs Placebo at preventing all cause diarrhea. From the graphs, there is little to no statistical or clinical difference between the two at treating all-cause diarrhea.

Currently, brain tissue atrophy serves as in vivo structural MRI biomarker of neurodegeneration in Alzheimer Disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that AD-related memory loss begins when neurons are lost. Hence, in vivo detection of neurodegeneration preceding detectable atrophy is essential for early AD diagnosis. Our innovative approach to assessing neuronal damage in vivo is based on (a) quantitative Gradient Recalled Echo (qGRE) MRI technique and (b) genetically-informed relationships between qGRE metrics and major components of brain tissue cellular structure (neurons/neurites and glia) that were obtained using gene expression profiles across the human brain provided by the Allen Human Brain Atlas. Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®)=0, amyloid β (Aβ)-negative), n=34] HC; Preclinical AD (CDR=0, Aβ-positive, n=19), PC; and mild AD (CDR=0.5 or 1, Aβ-positive, n=17), AD. Preliminary data show that qGRE identified new biomarkers in AD brain characterizing tissues with significantly lower (termed Dark Matter) and relatively preserved (termed Viable Tissue) concentrations of neurons. The fraction of Dark Matter showed clinically important and significant differences between HC and PC groups in hippocampus, middle temporal, fusiform, postcentral, and inferior parietal brain regions. However, no HC vs. PC group difference was detected by the volumetric measurements (Figure 1). Further, measurements of the Dark Matter fraction in the hippocampus showed strong associations with Cerebrospinal fluid (CSF) amyloid Aβ42 (r=-0.52), CSF ptau (r=0.60), 18 F-AV1415-PET tau imaging (r=0.60), and PiB-PET amyloid imaging (r=0.60). These associations were significantly stronger compared with volumetric measurements: CSF amyloid Aβ42 (r=0.18), CSF ptau (r=-0.24), PET tau imaging (r=-0.39), and PiB-PET amyloid imaging (r=-0.17)). Importantly, the Dark Matter fraction had a significant inter-regional association (r=0.87) with neuronal count in the hippocampal subfields obtained from histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration (Figure 2). qGRE-based measurements identify the microstructural changes in early AD-related neurodegeneration that are not recognized by structural MRI atrophy measurements, therefore providing new biomarkers for early AD detection.

OBJECTIVE:The purpose of our study was to perform a systematic review and meta-analysis of randomized, blinded, placebo-controlled studies that, following third-molar extraction, utilized either a combination of acetaminophen (600 mg) with codeine (60 mg) or ibuprofen (400 mg) for pain management. DESIGN:We searched PubMed, and the trial registry ClinicalTrials.gov databases with the keywords "molar or molars," "tooth or teeth," "extraction," and "pain." Selected studies were: (1) randomized, blinded, placebo controlled, (2) utilized either a single-dose combination acetaminophen (600 mg) with codeine (60 mg) (A/C) or ibuprofen, and (3) recorded standardized pain relief (PR) at 6 hours, or summed total pain relief over 6 hours (TOTPAR6). Of the 2,949 articles that were identified, 79 were retrieved for full-text analysis, and 20 of these studies met our inclusion criteria. RESULTS:For A/C, the weighted, standardized mean difference (SMD) for TOTPAR6 was 0.796 (95% confidence interval [CI], 0.597-0.995), P < .001, and for PR at 6 hours, the SMD was 0.0186 (0.007 to 0.378; P = .059), whereas for ibuprofen the SMD for TOTPAR6 was 3.009 (1.283 to 4.735; P = .001), and for PR at 6 hours, the SMD was 0.854 (95% CI, 0.712-0.996; P < .001). A SMD of 0.8 or larger is indicative of a large effect. CONCLUSIONS:Our data indicate that single dose of ibuprofen (400 mg) is an effective pain reducer for post third molar extraction pain.

Background: Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer’s disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis. Objective: To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus. Methods: Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17). Results: In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as “Dark Matter”, and the other, with relatively preserved neurons, “Viable Tissue”. Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration. Conclusion: In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

Objective: To evaluate regional calf muscle microcirculation in people with diabetes mellitus (DM) with and without foot ulcers, compared to healthy control people without DM, using contrast-free magnetic resonance imaging methods. Methods: Three groups of subjects were recruited: non-DM controls, DM, and DM with foot ulcers (DM + ulcer), all with ankle brachial index (ABI) > 0.9. Skeletal muscle blood flow (SMBF) and oxygen extraction fraction (SMOEF) in calf muscle were measured at rest and during a 5-min isometric ankle plantarflexion exercise. Subjects completed the Yale physical activity survey. Results: The exercise SMBF (ml/min/100 g) of the medial gastrocnemius muscle were progressively impaired: 63.7 ± 18.9 for controls, 42.9 ± 6.7 for DM, and 36.2 ± 6.2 for DM + ulcer, p < 0.001. Corresponding exercise SMOEF was the lowest in DM + ulcers (0.48 ± 0.09). Exercise SMBF in the soleus muscle was correlated moderately with the Yale physical activity survey ( r = 0.39, p < 0.01). Conclusions: Contrast-free MR imaging identified progressively impaired regional microcirculation in medial gastrocnemius muscles of people with DM with and without foot ulcers. Exercise SMBF in the medial gastrocnemius muscle was the most sensitive index and was associated with HbA1c. Lower exercise SMBF in the soleus muscle was associated with lower Yale score.

PURPOSE/OBJECTIVES:To determine the perceptions about the ill-effects of nicotine in students and faculty at a Midwestern dental school. This information will help inform the school and improve teaching on this subject during a time when electronic nicotine delivery systems are increasingly popular.METHODS:An online survey of dental students and faculty of a Midwestern dental school was deployed in November, 2020 to determine their level of misperception about the ill effects of nicotine. An online Qualtrics survey was administered to approximately 212 predoctoral students at a dental institution and approximately 100 part- and full-time faculty at the same school.RESULTS:The response rate for faculty was 55.1% and that for students was 37.5%. The majority of faculty and students "agreed" or "strongly agreed" that nicotine causes cancer, birth defects, cardiovascular disease, oral inflammation, and Chronic Obstructive Pulmonary Disease.CONCLUSIONS:Dental school faculty and students linked the risks of smoking tobacco to nicotine. Based on the results of this study, we feel our institution's curriculum should consider including information specific to nicotine in addition to tobacco in general.

Introduction Due to COVID-19, innovative, virtual educational methods are being developed to provide students with learning experiences comparable to established clinical practices. Our objective was to produce the Periodontal Senior Case Clinical Challenge (PSCCC) that would provide fourth-year students an alternative for senior case presentations and would be a formative assessment for which student opinions would be provided and analysed. The PSCCC would utilise an online, case-based, written, clinical assessment and follow-up, structured discussion to challenge students to demonstrate ability to apply didactic periodontal knowledge to patient-based experiences. We hypothesised the PSCCC would provide effective learning and a formative assessment. Material and Methods Relevant didactic resources were distributed to 48 students for independent review. The PSCCC was delivered in two sections, (1) a case-based assessment via a virtual classroom with written student responses, and (2) oral discussions conducted via virtual meetings that were moderated and assessed by ten periodontists, with the collaboration of nine residents. A voluntary six-statement survey was used to evaluate the students' opinions of the PSCCC. The scores for 75% (36/48) of students who participated were evaluated for statistical and clinical importance. Results The value of our PSCCC was supported by 91.7% (33/36) of the analyses (p < .0008). Discussion The PSCCC was a successful alternative pathway to assess students' clinical and didactic integrated knowledge in periodontics. It provided a unified vision of treatment of the selected case, building on all aspects of the students' periodontal education whilst allowing interaction in a simultaneous, three-tiered educational approach, involving dental students, periodontal residents and faculty. Conclusion In support of our hypothesis, for each of the 6 statements, >= 94.44% (34/36) of the scores given by the students were considered exceptionally strong clinical support for our pedagogical strategy that combines educational resources and can be successfully implemented in other programmes.

Selection Criteria The search was conducted by 2 independent reviewers, using 4 databases (PubMed [Medline], Embase, Cochrane Library, and Science Direct), without restrictions on date of publication or language. Gray literature resources were also searched (www.opengrey.eu/, https://openaire.eu/, and Research gate). In addition, the authors conducted a manual search of the Journal of Clinical Periodontology and the Journal of Periodontology for issues published from January 2015 to July 2017. The selection criteria included randomized clinical trials, controlled clinical trials, and observational studies (case series, cohort studies, and cross-sectional studies) that included >= 10 adult (age >= 18 years) Caucasians. Additional inclusion criteria were as follows: subjects without systemic diseases or medications who were diagnosed with gingivitis or chronic periodontitis, those diagnosed with vitamin D sufficiency, deficiency, and insufficiency confirmed with a blood test who were receiving initial nonsurgical periodontal therapy and not taking vitamin D supplementation before the inclusion in the study, those with chronic periodontitis taking vitamin D supplementation during initial nonsurgical periodontal treatment, and those with gingivitis who were taking vitamin D. Key Study Factor This review focused on studies that analyzed any association between serum vitamin D levels and gingivitis or periodontitis in healthy humans. All studies were limited to adult Caucasians (age >= 18 years) with normal serum vitamin D levels who were compared with adult Caucasians with serum vitamin D deficiency; however, the 7 studies included in the systematic review consisted of 4 case-control studies, 2 cross-sectional studies, and 1 intervention study. In 3 of the 7 studies, the serum level for deficiency was not given, and only 2 of the remaining 4 studies used the same level for deficiency, plus various types of assays were used to determine 25(OH)D serum levels; thus, there was considerable heterogeneity among the included studies in the definitions of normal versus deficient. Main Outcome Measure The main outcome measure was an assessment of any association between serum vitamin D levels and periodontal status (based on clinical parameters). For five of the seven studies included in the systematic review, "n/a" was listed as the diagnostic criteria for gingivitis, and the diagnostic criteria differed in the remaining two studies. The diagnostic criteria for chronic periodontitis were not reported in one study and were listed as "n/a" for another study. In the remaining five studies, the diagnostic criteria varied; thus, there was considerable heterogeneity among the included studies in the definitions for gingivitis and periodontitis. Main Results A total of 365 possibly eligible studies were initially identified from the electronic search. Manual search did not detect any further study for inclusion. From the initial search, 24 potentially relevant studies were evaluated, with only 7 included in the final analyses. Quality, risk of bias, and heterogenicity of the studies were assessed using the Newcastle-Ottawa Scale. All but 1 of the studies reported confounders such as age, gender, body mass index, and smoking status; however, only 2 studies adjusted for these in the logistic regression models that were used to estimate the association between serumvitaminD levels and periodontal status. There was considerable methodological heterogeneity in the studies, and this heterogeneity included different classifications for assessing periodontal status, variability in 25(OH)D assays, as well as different cutoff points for vitamin D adequacy, deficiency, and insufficiency. In one study, the authors also detected inconsistencies in how vitamin D values were reported, and in another study, the authors determined that there was poor follow-up, which made the interpretation of results problematic. Based on their systematic review, the authors stated that 4 case-control studies showed an influence of vitamin D and its metabolites on periodontal status, and 1 experimental study (uncontrolled trial) suggested the proposed anti-inflammatory role of vitamin D. In addition, 2 cross-sectional studies failed to show a relationship between vitamin D and periodontal condition; however, the findings of one of the cross-sectional studies indicate "the individuals with low levels of vitamin D would be susceptible to gingivitis", and the other cross-sectional study found beneficial effects of serum vitamin D levels in periodontitis, although the effects were reported as not being statistically significant. It, therefore, appears that all 7 articles included in the systematic review found that vitamin D has a potentially beneficial effect in adult periodontal disease. Conclusion Although there were methodological heterogeneity and limitations in the studies selected for this systematic review, it was determined that there is support for a protective role of serum vitamin D levels in adults who have periodontal disease.

OBJECTIVE. The purpose of this study was to investigate the reproducibility of three quantitative MRI parameters associated with patellar instability and to determine whether they measure anatomic predisposition to patellar instability individually or in combination with the other parameters. MATERIALS AND METHODS. In this retrospective study, 100 patients diagnosed with a patellar dislocation injury and 100 age- and sex-matched control patients were examined using MRI. The distance between the tibial tubercle and posterior cruciate ligament (TT-PCL), distance between the tibial tubercle and trochlear groove (TT-TG), and TG depth (trochlear dysplasia) were measured independently by three fellowship-trained musculoskeletal radiologists. Intraclass correlation coefficient (ICC) was used to assess intraobserver and interobserver reliability. The parameters in both groups were tested for interdependence on each other and were compared for prevalence and association with patellar instability. RESULTS. All three parameters showed almost perfect intraobserver (TT-PCL ICC, ≥ 0.88; TT-TG ICC, 0.96; trochlear dysplasia ICC, ≥ 0.92) and interobserver (TT-PCL ICC, 0.82; TT-TG ICC, 0.94; trochlear dysplasia ICC, 0.91) reliability and were significantly more common in the patellar instability group. Trochlear dysplasia had the highest association with patellar instability, both as a unique parameter and in pairwise combination with an abnormal TT-TG. Optimal cutoff thresholds for normal TT-TG and TT-PCL were 15.00 mm or less and 21.30 mm or less, respectively. The optimal normal cutoff threshold for evaluating trochlear dysplasia via trochlear depth was 4.95 mm or more. CONCLUSION. Patellar instability is multifactorial. Highly reproducible parameters derived from MRI reveal both unique and overlapping anatomic predispositions, and considering all parameters together may help individualize patient management when selecting orthopedic procedures.

INTRODUCTION:Dental and oral health researchers compose a small share of the research workforce, and within this group female researchers form a much smaller share than male researchers. Additionally, a majority of full-time faculty appointments at dental schools are held by men, with women making up only 39% of full-time appointments. These factors suggest that there could be disparities between men and women in obtaining research funding.OBJECTIVE:The focus of our study was to determine whether there are gender differences in award funding obtained from the National Institute of Dental and Craniofacial Research or the National Institutes of Health (NIH).METHODS:NIH administrative data were analyzed by focusing on Research Project Grants (RPGs), the primary and most commonly used mechanism to support investigator-initiated research projects. Analyses involved 1 or 2 of the following variables: number of unique applicants or awardees, fiscal years 2007 to 2016, average age of unique applicants, awardee's degrees, awardee's age at first R01, and award rates.RESULTS:About two-thirds of RPG applicants and awardees were men. Although there were significantly more male applicants and awardees, there was no significant difference in award rate by gender, and there was no significant award rate variation through time or by degrees. The average ages of RPG applicants were similar for genders for all degrees, except that male dentists and PhD-dentists applying to the National Institute of Dental and Craniofacial Research were older and male MDs and PhD-dentists from dental schools applying to the NIH were older.CONCLUSIONS:This study demonstrated that men in the dental/oral health workforce submit more applications and receive more NIH awards than do women; however, there was no difference in award rates between women and men and no difference in ages by gender at which the first R01 awards are received.KNOWLEDGE TRANSFER STATEMENT:Analyses of the implications of this study by the academic dentistry and oral health community could lead to establishing opportunities to expand the representation of women in dental and oral health research. Increasing the number of applications submitted by women may help achieve an equitable balance of grantees in the workforce.

PURPOSE:To evaluate the efficacy of SmartMouth Clinical DDS compared with 0.12% chlorhexidine and placebo mouthrinses. MATERIALS AND METHODS:Seventy-six subjects with gingivitis or chronic periodontitis were enrolled in a double-blind, placebo-controlled, clinical study. Examinations included Gingival Index (GI), Bleeding Score (BS), Plaque Index (PI), Tooth Stain Index (TSI), and Calculus Index (CI). Subjects were given a prophylaxis and oral hygiene instructions at the time of enrolment. Subjects were assigned to one of three groups: SmartMouth Clinical DDS (SM), 0.12% chlorhexidine (CHX), or placebo (PL). Subjects were examined at 3 and 6 weeks. Data were evaluated as differences from baseline for each group. Analysis of variance (ANOVA), t tests or non-parametric alternatives were used to analyse data. RESULTS:The GI, BS and PI decreases from baseline were statistically significant at both 3 and 6 weeks for all three groups (p ≤ 0.025). Differences between groups were not statistically significant, except that the PI decrease for CHX was significantly greater than PL at 6 weeks (p = 0.048). At 6 weeks there was a statistically significant increase in TSI for CHX (p ≤ 0.001). CI decreased significantly for all groups at 3 weeks (p ≤ 0.004) and for PL at 6 weeks (p ˂ 0.001). At 3 weeks and 6 weeks, the percentages for compliance were significantly higher for SM and PL than for CHX (p ˂ 0.001). SM had less taste alteration reported than CHX (p = 0.003). CONCLUSION:While all three groups were shown to improve GI, BS and PI scores; non-prescription SM resulted in less taste alteration, less tooth stain and better compliance than CHX.