IMPORTANCE Lower educational attainment is associated with increased risk of adverse pregnancy outcomes, but it is unclear which pathways mediate this association. OBJECTIVE To investigate the association between educational attainment and pregnancy outcomes and the proportion of this association that is mediated through modifiable cardiometabolic risk factors. DESIGN, SETTING, AND PARTICIPANTS In this 2-sample mendelian randomization (MR) cohort study, uncorrelated (R-2 < 0.01) single-nucleotide variants (formerly single-nucleotide polymorphisms) associated with the exposure (P < 5 x 10(-8)) and mediators and genetic associations with the pregnancy outcomes from genome-wide association studies were extracted. All participants were of European ancestry and were largely from Finland, Iceland, the United Kingdom, or the US. The inverse variance-weighted method was used in the main analysis, and the weighted median, weighted mode, and MR Egger regression were used in sensitivity analyses. In mediation analyses, the direct effect of educational attainment estimated in multivariable MR was compared with the total effect estimated in the main univariable MR analysis. Data were extracted between December 1, 2022, and April 30, 2023. EXPOSURE Genetically estimated educational attainment. The mediators considered were genetically estimated type 2 diabetes, body mass index, smoking, high-density lipoprotein cholesterol level, and systolic blood pressure. MAIN OUTCOMES AND MEASURES Ectopic pregnancy, hyperemesis gravidarum, gestational diabetes, preeclampsia, preterm birth, and offspring birth weight. RESULTS The analyses included 3 037499 individuals with data on educational attainment, and those included in studies on pregnancy outcomes ranged from 141014 for ectopic pregnancy to 270 002 with data on offspring birth weight. Each SD increase in genetically estimated educational attainment (ie, 3.4 years) was associated with an increased birth weight of 42 (95% CI, 28-56) g and an odds ratio ranging from 0.53 (95% CI, 0.46-0.60) for ectopic pregnancy to 0.81(95% CI, 0.71-0.93) for preeclampsia. The combined proportion of the association that was mediated by the 5 cardiometabolic risk factors ranged from -17% (95% CI, -46% to 26%) for hyperemesis gravidarum to 78% (95% CI, 10%-208%) for preeclampsia. Sensitivity analyses accounting for pleiotropy were consistent with the main analyses. CONCLUSIONS AND RELEVANCE In this MR cohort study, intervening for type 2 diabetes, body mass index, smoking, high-density lipoprotein cholesterol level, and systolic blood pressure may lead to reductions in several adverse pregnancy outcomes associated with lower levels of education. Such public health interventions would serve to reduce health disparities attributable to social inequalities.

IntroductionIn recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical trials. The objective of this scoping review is to systematically map, describe and summarise the current utilisation of artificial intelligence in recruitment and retention process of clinical trials that has been reported in research. Additionally, the review aims to identify benefits and drawbacks, as well as barriers and facilitators associated with the application of artificial intelligence in optimising recruitment and retention in clinical trials. The findings of this review will provide insights and recommendations for future development of artificial intelligence in the context of clinical trials.Methods and analysisThe review of relevant literature will follow the methodological framework for scoping studies provided by the Joanna Briggs Institute. A comprehensive electronic search will be conducted using the search strategy developed by the authors. Leading medical and computer science databases such as PubMed, Embase, Scopus, IEEE Xplore and Web of Science Core Collection will be searched. The search will encompass analytical observational studies, descriptive observational studies, experimental and quasi-experimental studies published in all languages, without any time limitations, which use artificial intelligence tools in the recruitment and retention process of clinical trials. The review team will screen the identified studies and import them into a dedicated electronic library specifically created for this review. Data extraction will be performed using a data charting table.Ethics and disseminationSecondary data will be attained in this scoping review; therefore, no ethical approval is required. The results of the final review will be published in a peer-reviewed journal. It is expected that results will inform future artificial intelligence and clinical trials research.

OBJECTIVES:Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature. STUDY DESIGN AND SETTING:We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/). RESULTS:Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods. CONCLUSION:In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data.

ABSTRACTBackgroundNon‐Hodgkin lymphoma (NHL) is one of the most common haematologic malignancies in the world. Despite substantial efforts to identify causes and risk factors for NHL, its aetiology is largely unclear. Autoimmune diseases have long been considered potential risk factors for NHL. We carried out Mendelian randomisation (MR) analyses to examine whether genetically predicted susceptibility to ten autoimmune diseases (Behçet's disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sjögren's syndrome, systemic sclerosis, and type 1 diabetes) is associated with risk of NHL.MethodsTwo‐sample MR was performed using publicly available summary statistics from cohorts of European ancestry. For NHL and four NHL subtypes, we used data from UK Biobank, Kaiser Permanente cohorts, and FinnGen studies.ResultsNegative associations between type 1 diabetes and sarcoidosis and the risk of NHL were observed (odds ratio [OR] 0.95, 95% confidence interval [CI]: 0.92–0.98, p = 5 × 10−3, and OR 0.92, 95% CI: 0.85–0.99, p = 2.8 × 10−2, respectively). These findings were supported by the sensitivity analyses accounting for potential pleiotropy and weak instrument bias. No significant associations were found between the other eight autoimmune diseases and NHL risk.ConclusionThese findings suggest that genetically predicted susceptibility to type 1 diabetes, and to some extent sarcoidosis, might reduce the risk of NHL. However, future studies with different datasets, approaches, and populations are warranted to further examine the potential associations between these autoimmune diseases and the risk of NHL.

ABSTRACT Objective To examine whether genetically predicted susceptibility to ten autoimmune diseases (Behçet’s disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sjögren’s syndrome, systemic sclerosis, and type 1 diabetes) is associated with risk of non-Hodgkin lymphoma (NHL). Design Two sample Mendelian randomization (MR) study. Setting Genome wide association studies (GWASs) of ten autoimmune diseases, NHL, and four NHL subtypes (i.e., follicular lymphoma, mature T/natural killer-cell lymphomas, non-follicular lymphoma, and other and unspecified types of NHL). Analysis We used data from the largest publicly available GWASs of European ancestry for each autoimmune disease, NHL, and NHL subtypes. For each autoimmune disease, we extracted single nucleotide polymorphisms (SNPs) strongly associated ( P < 5×10 −8 ) with that disease and that were independent of one another (R 2 < 1×10 −3 ) as genetic instruments. SNPs within the human leukocyte antigen region were not considered due to potential pleiotropy. Our primary MR analysis was the inverse-variance weighted analysis. Additionally, we conducted MR-Egger, weighted mode, and weighted median regression to address potential bias due to pleiotropy, and robust adjusted profile scores to address weak instrument bias. We carried out sensitivity analysis limited to the non-immune pathway for nominally significant findings. To account for multiple testing, we set the thresholds for statistical significance at P < 5×10 −3 . Participants The number of cases and controls identified in the relevant GWASs were 437 and 3,325 for Behçet’s disease, 4,918 and 5,684 for coeliac disease, 435 and 341,188 for dermatitis herpetiformis, 4,576 and 8,039 for lupus, 11,988 and 275,335 for psoriasis, 22,350 and 74,823 for rheumatoid arthritis, 3,597 and 337,121 for sarcoidosis, 2,735 and 332,115 for Sjögren’s syndrome, 9,095 and 17,584 for systemic sclerosis, 18,942 and 501,638 for type 1 diabetes, 2,400 and 410,350 for NHL; and 296 to 2,340 cases and 271,463 controls for NHL subtypes. Exposures : Genetic variants predicting ten autoimmune diseases: Behçet’s disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sjögren’s syndrome, systemic sclerosis, and type 1 diabetes. Main outcome measures Estimated associations between genetically predicted susceptibility to ten autoimmune diseases and the risk of NHL. Results The variance of each autoimmune disease explained by the SNPs ranged from 0.3% to 3.1%. Negative associations between type 1 diabetes and sarcoidosis and the risk of NHL were observed (odds ratio [OR] 0.95, 95% confidence interval [CI]: 0.92 to 0.98, P = 5×10 -3 , and OR 0.92, 95% CI: 0.85 to 0.99, P = 2.8×10 -2 , respectively). These findings were supported by the sensitivity analyses accounting for potential pleiotropy and weak instrument bias. No significant associations were found between the other eight autoimmune diseases and NHL risk. Of the NHL subtypes, type 1 diabetes was most strongly associated with follicular lymphoma (OR 0.91, 95% CI: 0.86 to 0.96, P = 1×10 -3 ), while sarcoidosis was most strongly associated with other and unspecified NHL (OR 0.86, 95% CI: 0.75 to 0.97, P = 1.8×10 -2 ). Conclusions These findings suggest that genetically predicted susceptibility to type 1 diabetes, and to some extent sarcoidosis, might reduce the risk of NHL. However, future studies with different datasets, approaches, and populations are warranted to further examine the potential associations between these autoimmune diseases and the risk of NHL. WHAT IS ALREADY KNOWN ON THIS TOPIC The etiology of non-Hodgkin lymphoma, a common hematological malignancy, is not fully understood. Observational studies have reported statistically significant associations between ten autoimmune diseases (Behçet’s disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sjögren’s syndrome, systemic sclerosis, and type 1 diabetes) and risk of non-Hodgkin lymphoma, but these studies may be susceptible to residual confounding and reverse causation. WHAT THIS STUDY ADDS Genetically predicted susceptibility to type 1 diabetes, and to some extent sarcoidosis, may be associated with a reduced risk of non-Hodgkin lymphoma, while no clear associations were observed between the other eight autoimmune diseases and risk of non-Hodgkin lymphoma or its subtypes. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY Using an approach that seeks to address residual confounding and reverse causation, these findings contradict previously reported associations between autoimmune diseases and risk of non-Hodgkin lymphoma from traditional observational studies. Future studies with different datasets, approaches, and populations are warranted to further examine the potential associations between these autoimmune diseases and the risk of NHL.

ABSTRACT Objective To investigate the relationship between education and pregnancy outcomes, and the proportion of the effect of education mediated through modifiable cardiometabolic risk factors, using two-sample Mendelian randomization (MR) analyses. Methods and Analysis We extracted uncorrelated ( R 2 <0.01) single-nucleotide polymorphisms strongly associated (p-value <5e-8) with educational attainment, type 2 diabetes mellitus, body mass index, smoking, high-density lipoprotein cholesterol, and systolic blood pressure from the largest genome-wide association studies with available summary data. Genetic associations with ectopic pregnancy, hyperemesis gravidarum, gestational diabetes, preeclampsia, preterm birth, and offspring birth weight were extracted from the largest genome-wide association studies with available summary data. All subjects were of European ancestry. We conducted univariable MR analyses with the inverse-variance weighted method employed in the main analysis, and weighted median, weighted mode and MR Egger regression in the sensitivity analyses to account for potential pleiotropy. In mediation analyses, we compared the direct effect of educational attainment estimated in multivariable MR with the total effect estimated in the main univariable MR analysis. Results The analyses included more than 3 million subjects with data on educational attainment, 270,002 subjects with data on offspring birth weight, and between 2,092 and 15,419 cases with adverse pregnancy outcomes. Each standard deviation increase in genetically-predicted educational attainment (3.4 years) was associated with an increased birth weight (95% confidence interval) of 42 g (28 g to 56 g) and an odds ratio (95% confidence interval) of 0.53 (0.46 to 0.60) for ectopic pregnancy, 0.54 (0.44 to 0.66) for hyperemesis gravidarum, 0.73 (0.67 to 0.80) for gestational diabetes, 0.81 (0.71 to 0.93) for preeclampsia, and 0.72 (0.67 to 0.77) for preterm birth. The combined proportion of the effect (95% confidence interval) of genetically-predicted educational attainment that was mediated by the five cardiometabolic risk factors was 42% (14% to 59%) for ectopic pregnancy, -17% (-46% to 26%) for hyperemesis gravidarum, 48% (19% to 82%) for gestational diabetes, 78% (10% to 208%) for preeclampsia, 28% (0% to 51%) for preterm birth, and 9% (-26% to 24%) for birth weight. Sensitivity analyses accounting for pleiotropy were consistent with the main analyses. Conclusion Our findings support that intervening on type 2 diabetes mellitus, body mass index, smoking, high-density lipoprotein cholesterol, and systolic blood pressure would lead to reductions in several adverse pregnancy outcomes attributable to lower levels of education. Such public health interventions would serve to reduce health disparities attributable to social inequalities. BOX What is Already Known on This Topic Lower educational attainment is linked to increased risk of adverse pregnancy outcomes, and cardiometabolic risk factors are suspected to mediate some of this effect. What This Study Adds Our findings from using a two-sample Mendelian randomization approach are in support of a causal relationship between lower educational attainment increasing risk of ectopic pregnancy, hyperemesis gravidarum, gestational diabetes, preeclampsia, preterm birth and offspring low birth weight. A sizeable portion of the effect of educational attainment on ectopic pregnancy, gestational diabetes, preeclampsia and preterm birth is mediated by type 2 diabetes mellitus, body mass index, smoking, high-density lipoprotein cholesterol and systolic blood pressure, while these cardiometabolic risk factors combined explain little of the effect on hyperemesis gravidarum or low birth weight. How This Study Might Affect Research, Practice, or Policy The effects of socioeconomic inequalities on risk of ectopic pregnancy, gestational diabetes, preeclampsia and preterm birth can be reduced by intervening on type 2 diabetes mellitus, body mass index, smoking, high-density lipoprotein cholesterol and systolic blood pressure.

BackgroundAlthough the number of cancer clinical drug trials is increasing rapidly in China, issues concerning informed consent in this research context are understudied. By performing a narrative literature review, we aim to describe the current situation and identify the most salient challenges affecting informed consent in cancer clinical drug trials among adult patients in China since 2000.MethodsWe searched Web of Science (WOS), PubMed, Scopus, EMBASE, the Cochrane Library databases, China National Knowledge Infrastructure (CNKI), China Biomedical Literature Database on Disc (CBMdisc), Chinese Scientific Journals Fulltext Database (CQVIP), and WANFANG Data to identify relevant publications since 2000. Data were extracted by three reviewers on six items pertaining to study type, theme, and challenges.ResultsWe identified 37 unique manuscripts, from which 19 full texts were obtained and six were included in the review. All six studies were published in Chinese journals, and the publication years of the majority (five out of six) of the studies were 2015 or later. The authors of the six studies were all from clinical departments or ethical review committees at five hospitals in China. All of the included publications were descriptive studies. Publications reported challenges related to the following aspects of informed consent: information disclosure, patient understanding, voluntariness, authorization, and procedural steps.ConclusionBased on our analysis of publications over the past two decades, there are currently frequent challenges related to various aspects of informed consent in cancer clinical drug trials in China. Furthermore, only a limited number of high-quality research studies on informed consent in cancer clinical drug trials in China are available to date. Efforts toward improvement of informed consent practice, in the form of guidelines or further regulations in China, should draw on both experience from other countries and high-quality local evidence.

Abstract Background In recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical trials. The objective of this protocol is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence in recruitment and retention process of clinical trials. Additionally, it aims to identify the benefits and drawbacks, as well as the barriers and facilitators associated with the application of artificial intelligence in optimizing recruitment and retention in clinical trials. The findings of this review will provide valuable insights and recommendations for future development of artificial intelligence in the context of clinical trials . Methods The review of relevant literature will follow the methodological framework for scoping studies provided by the Joanna Briggs Institute. A comprehensive electronic search will be conducted using the search strategy developed by the authors. Leading medical and computer science databases such as PubMed, Embase, Scopus, IEEE Xplore and Web of Science Core Collection (including SCI-EXPANDED, SSCI, A&HCI, ESCI), will be searched. The search will encompass all original research, descriptive studies, randomized controlled trials, pilot studies, conference papers, comments and feasible or acceptable studies published in English, without any time limitations, that utilize artificial intelligence tools in the recruitment and retention process of clinical trials. The review team will carefully screen the identified studies and import them into a dedicated electronic library specifically created for this review. Data extraction will be performed using a data charting table, which will include publication details, study design, and specific outcomes/results. Discussion The objective of this scoping review is to provide a comprehensive overview of current status of artificial intelligence tools utilized in recruitment and retention process of clinical trials. By examining the existing literature, we aim to identify the barriers and facilitators that have influenced the application of these tools. Through our study, we will uncover gaps in the current knowledge, which can guide future research efforts. Additionally, our findings will offer recommendations to stakeholders regarding the use of artificial intelligence tools and may contribute to the advancement of their application in clinical trials. Systematic review registration: Open Science Framework https://osf.io/c9yhk/

Systematic reviewsandmeta-analyses,which identify and synthesise evidence from individual studies, are often believed to provide an overview of the best available evidence on a specific research question. In epidemiology, however, systematic reviews and meta-analyses typically focus on individual exposure to outcome relationships, which can fail to capture all potentially related exposures or outcomes across an entire field. Moreover, concerns have consistently been raisedabout thegrowingnumber of overlapping and conflicting reviews.1 2 These limitations emphasise the need for a study design that can potentially provide a higher level synthesis of summary level evidence.1 2

Abstract Objective To characterize potential drug safety signals identified from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), from 2008 to 2019, to determine how often these signals resulted in regulatory action by the FDA and whether these actions were corroborated by published research findings or public assessments by the Sentinel Initiative. Design Cross sectional study. Setting USA. Population Safety signals identified from the FAERS and publicly reported by the FDA between 2008 and 2019; and review of the relevant literature published before and after safety signals were reported in 2014-15. Literature searches were performed in November 2019, Sentinel Initiative assessments were searched in December 2021, and data analysis was finalized in December 2021. Main outcome measures Safety signals and resulting regulatory actions; number and characteristics of published studies, including corroboration of regulatory action as evidenced by significant associations (or no associations) between the drug related to the signal and the adverse event. Results From 2008 to 2019, 603 potential safety signals identified from the FAERS were reported by the FDA (median 48 annually, interquartile range 41-61), of which 413 (68.5%) were resolved as of December 2021 (372 of 399 (93.2%) signals ≥3 years old were resolved). Among the resolved safety signals, 91 (22.0%) led to no regulatory action and 322 (78.0%) resulted in regulatory action, including 319 (77.2%) changes to drug labeling and 59 (14.3%) drug safety communications or other public communications from the FDA. For a subset of 82 potential safety signals reported in 2014-15, a literature search identified 1712 relevant publications; 1201 (70.2%) were case reports or case series. Among these 82 safety signals, 76 (92.7%) were resolved, of which relevant published research was identified for 57 (75.0%) signals and relevant Sentinel Initiative assessments for four (5.3%) signals. Regulatory actions by the FDA were corroborated by at least one relevant published research study for 17 of the 57 (29.8%) resolved safety signals; none of the relevant Sentinel Initiative assessments corroborated FDA regulatory action. Conclusions Most potential safety signals identified from the FAERS led to regulatory action by the FDA. Only a third of regulatory actions were corroborated by published research, however, and none by public assessments from the Sentinel Initiative. These findings suggest that either the FDA is taking regulatory actions based on evidence not made publicly available or more comprehensive safety evaluations might be needed when potential safety signals are identified.

This cross-sectional study compares the author and journal characteristics of retracted articles on COVID-19 with retracted articles from other topics.

ObjectivesTo summarise the range, strength, and validity of reported associations between environmental risk factors and non-Hodgkin's lymphoma, and to evaluate the concordance between associations reported in meta-analyses of summary level data and meta-analyses of individual participant data.DesignUmbrella review and comparison of meta-analyses of summary and individual participant level data.Data sourcesMedline, Embase, Scopus, Web of Science Core Collection, Cochrane Library, and Epistemonikos, from inception to 23 July 2021.Eligibility criteria for selecting studiesEnglish language meta-analyses of summary level data and of individual participant data evaluating associations between environmental risk factors and incident non-Hodgkin's lymphoma (overall and subtypes).Data extraction and synthesisSummary effect estimates from meta-analyses of summary level data comparing ever versus never exposure that were adjusted for the largest number of potential confounders were re-estimated using a random effects model and classified as presenting evidence that was non-significant, weak (P<0.05), suggestive (P<0.001 and >1000 cases), highly suggestive (P<0.000001, >1000 cases, largest study reporting a significant association), or convincing (P<0.000001, >1000 cases, largest study reporting a significant association, I2<50%, 95% prediction interval excluding the null value, and no evidence of small study effects and excess significance bias) evidence. When the same exposures, exposure contrast levels, and outcomes were evaluated in meta-analyses of summary level data and meta-analyses of individual participant data from the International Lymphoma Epidemiology (InterLymph) Consortium, concordance in terms of direction, level of significance, and overlap of 95% confidence intervals was examined. Methodological quality of the meta-analyses of summary level data was assessed by the AMSTAR 2 tool.ResultsWe identified 85 meta-analyses of summary level data reporting 257 associations for 134 unique environmental risk factors and 10 subtypes of non-Hodgkin's lymphoma nearly all (79, 93%) were classified as having critically low quality. Most associations (225, 88%) presented either non-significant or weak evidence. The 11 (4%) associations presenting highly suggestive evidence were primarily for autoimmune or infectious disease related risk factors. Only one association, between history of coeliac disease and risk of non-Hodgkin's lymphoma, presented convincing evidence. Of 40 associations reported in meta-analyses of summary level data that were also evaluated in InterLymph meta-analyses of individual participant data, 22 (55%) pairs were in the same direction, had the same level of statistical significance, and had overlapping 95% confidence intervals; 28 (70%) pairs had summary effect sizes from the meta-analyses of individual participant data that were more conservative.ConclusionThis umbrella review suggests evidence of many meta-analyses of summary level data reporting weak associations between environmental risk factors and non-Hodgkin's lymphoma. Improvements to primary studies as well as evidence synthesis in evaluations of evironmental risk factors and non-Hodgkin's lymphoma are needed.Review registration numberPROSPERO CRD42020178010.

ImportancePreprints have been widely adopted to enhance the timely dissemination of research across many scientific fields. Concerns remain that early, public access to preliminary medical research has the potential to propagate misleading or faulty research that has been conducted or interpreted in error.ObjectiveTo evaluate the concordance among study characteristics, results, and interpretations described in preprints of clinical studies posted to medRxiv that are subsequently published in peer-reviewed journals (preprint-journal article pairs).Design, Setting, and ParticipantsThis cross-sectional study assessed all preprints describing clinical studies that were initially posted to medRxiv in September 2020 and subsequently published in a peer-reviewed journal as of September 15, 2022.Main Outcomes and MeasuresFor preprint-journal article pairs describing clinical trials, observational studies, and meta-analyses that measured health-related outcomes, the sample size, primary end points, corresponding results, and overarching conclusions were abstracted and compared. Sample size and results from primary end points were considered concordant if they had exact numerical equivalence.ResultsAmong 1399 preprints first posted on medRxiv in September 2020, a total of 1077 (77.0%) had been published as of September 15, 2022, a median of 6 months (IQR, 3-8 months) after preprint posting. Of the 547 preprint-journal article pairs describing clinical trials, observational studies, or meta-analyses, 293 (53.6%) were related to COVID-19. Of the 535 pairs reporting sample sizes in both sources, 462 (86.4%) were concordant; 43 (58.9%) of the 73 pairs with discordant sample sizes had larger samples in the journal publication. There were 534 pairs (97.6%) with concordant and 13 pairs (2.4%) with discordant primary end points. Of the 535 pairs with numerical results for the primary end points, 434 (81.1%) had concordant primary end point results; 66 of the 101 discordant pairs (65.3%) had effect estimates that were in the same direction and were statistically consistent. Overall, 526 pairs (96.2%) had concordant study interpretations, including 82 of the 101 pairs (81.2%) with discordant primary end point results.Conclusions and RelevanceMost clinical studies posted as preprints on medRxiv and subsequently published in peer-reviewed journals had concordant study characteristics, results, and final interpretations. With more than three-fourths of preprints published in journals within 24 months, these results may suggest that many preprints report findings that are consistent with the final peer-reviewed publications.

This cross-sectional study examines the concordance between clinical studies posted as preprints and subsequently published in high-impact journals, including key study characteristics, reported results, and study interpretations.

Objective To estimate the financial costs paid by individual medical researchers from meeting the article processing charges (APCs) levied by open access journals in 2019. Design Cross-sectional analysis. Data sources Scopus was used to generate two random samples of researchers, the first with a senior author article indexed in the ‘Medicine’ subject area (general researchers) and the second with an article published in the ten highest-impact factor general clinical medicine journals (high-impact researchers) in 2019. For each researcher, Scopus was used to identify all first and senior author original research or review articles published in 2019. Data were obtained from Scopus, institutional profiles, Journal Citation Reports, publisher databases, the Directory of Open Access Journals, and individual journal websites. Main outcome measures Median APCs paid by general and high-impact researchers for all first and senior author research and review articles published in 2019. Results There were 241 general and 246 high-impact researchers identified as eligible for our study. In 2019, the general and high-impact researchers published a total of 914 (median 2, IQR 1–5) and 1471 (4, 2–8) first or senior author research or review articles, respectively. 42% (384/914) of the articles from the general researchers and 29% (428/1471) of the articles from the high-impact medical researchers were published in fully open access journals. The median total APCs paid by general researchers in 2019 was US$191 (US$0–US$2500) and the median total paid by high-impact researchers was US$2900 (US$0–US$5465); the maximum paid by a single researcher in total APCs was US$30115 and US$34676, respectively. Conclusions Medical researchers in 2019 were found to have paid between US$0 and US$34676 in total APCs. As journals with APCs become more common, it is important to continue to evaluate the potential cost to researchers, especially on individuals who may not have the funding or institutional resources to cover these costs.

跨性别女性是艾滋病病毒(HIV)感染的高危人群,但国内针对此人群的研究较匮乏.本文针对该人群HIV感染风险的相关因素,主要包括性行为及其相关因素、精神心理因素、物质滥用、社会经济因素及医疗服务获取情况等进行综述,为了解跨性别人群及预防和控制HIV在该人群的传播提供参考.

Background:Highly sensitive, non-invasive, and easily accessible diagnostics for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) are essential for the control of the Coronavirus Disease 2019 (COVID-19) pandemic. There is a clear need to establish a gold standard diagnostic for SARS-CoV-2 infection in humans using respiratory tract specimens. Methods:Searches will be conducted in the bibliographic databases Medline, Embase, bioRxiv, medRxiv, F1000, ChemRxiv, PeerJ Preprints, Preprints.org, Beilstein Archive, and Research Square. Relevant government documents and grey literature will be sought on the FDA's Emergency Use Authorizations website, the ECDC's website, and the website of the Foundation for Innovative New Diagnostics. Finally, papers categorized as diagnosis papers by the EPPI Centre's COVID-19 living systematic map will be added to our screening process; those papers are tagged with the diagnosis topic based on human review, rather than database searches, and thus this set of papers might include ones that have not been captured by our search strategy.

Procrastination has been closely linked to psychosocial health problems, such as depression and anxiety, among college students. However, few studies have focused on the magnifying effects of multiple psychosocial health problems on procrastination. We conducted a cross-sectional study by convenience sampling among 509 college students in Shanghai, China. Logistic regressions were performed to assess the relationship between psychosocial variables and procrastination and to verify the syndemic effect of psychosocial factors. Univariate analyses revealed that self-esteem, depression, and loneliness were associated with procrastination. In multivariate analyses, self-esteem and depression remained significant. College students with four psychosocial problems were approximately 2.5 times more vulnerable to procrastination compared with non-syndemic (have no more than one problem) students. The study indicates that college students with more psychosocial health problems exhibit severer procrastination, which in turn suggests that psychosocial syndemic theory can be applied to procrastination.

Polybrominated diphenyl ethers (PBDEs), a kind of important Brominated Flame Retardant (BFR), are widely used in electronic products, construction materials and textiles. PBDEs have been detected in many environmental media (including air, water, dust, sediment and food), many animal and human tissues. For their environmental persistence, high bioaccumulative and multiple biotoxicitiies, PBDEs have been viewed as one of the most concerned environmental Endocrine Disrupting Chemicals (EDCs) at present. Although detailed mechanisms are not clear, studies have found that PBDEs can induce toxicity to liver, endocrine system, nervous system, reproduction and immune system. What's more, lots of experiments indicate that PBDEs exposure can alter the levels of thyroid hormones. Recently, studies on the impact of PBDEs exposure on thyroid hormones have been quite a few and have not reached an agreement, especially on the alternation of thyroid hormones caused by PBDEs exposure, which has also been a hot issue. This paper reviews from the basic properties, usage, exposure and biotoxicity of PBDEs. We mainly introduce the impact PBDEs have on the thyroid and thyroid hormones in terms of biotoxicity, and attach importance to the endocrine disruption and neurodeveloptoxicity. We also give a preliminary introduction to hydroxylated and methoxylated polybrominated diphenyl ethers, structural analogs of PBDEs, which researchers start late to study. This paper can be a reference for the further research on PBDEs exposure and biotoxicity.