Emergency medicine (EM) residents rotate through various other environments during their training which often have variable hours and schedule patterns. These variations likely affect the amount of sleep residents are able to get. This study aims to determine if EM residents at a level one trauma center sleep less during more time- intensive rotations. This was an IRB approved, prospective, observational study that obtained EM residents' sleep times using a Fitbit Charge 3 device. Each willing participant received a Fitbit device and was asked to wear it at all times for a three month period. Each resident's corresponding rotation for the month was also recorded and associated with their data. The device automatically records sleep time for each 24-hour period and syncs to a database that records sleep times for each subject number. Data was interpreted using descriptive statistics, calculating median times for each subject throughout each rotation. Medians were used in order to avoid unnecessary skew for single sleep time outliers. The average median sleep time was then calculated for each rotation. Sleep data was collected for 33 EM residents, 10 female and 23 male, with rotators through nine different environments: EM, medical intensive care unit (MICU), surgical intensive care unit (SICU), pediatric intensive care unit (PICU), trauma, elective, anesthesia, orthopedics, and obstetrics. EM rotators had an average median sleep time of 424.7 minutes (N=32), approximately 7.1 hours daily. Residents on SICU slept 391.8 minutes on average (N=7), orthopedics 398.2 minutes (N=3), trauma 399.8 minutes (N=5), MICU 418.4 minutes (N=8), anesthesia 438.3 minutes (N=6), PICU 439.5 minutes (N=3), elective 455.4 minutes (N=7), and obstetrics 482.8 minutes (N=2), with N representing the number of EM residents with sleep data for at least one month of that rotation. EM rotators had the most robust data set, with 32 out of 33 residents having at least one month of sleep data for emergency medicine. The average sleep time for rotators in EM fell between the other eight rotations. SICU rotators slept the least on average, 391.8 minutes or 6.5 hours. Both trauma and MICU rotators slept less on average than emergency medicine rotators. Orthopedics also had a lower average sleep time, however it is important to note that only three subjects had at least one month of sleep data for their orthopedics rotation, as this is classically a less time-consuming off-service rotation. SICU, orthopedics, trauma, and MICU all had average median sleep times less than seven hours daily. Overall, it does seem that EM residents at our institution sleep a varying amount based on their current rotation which likely affects other elements of lifestyle and wellness. Understanding why certain rotations lead to poorer sleep quantity could aid program leadership in making positive scheduling changes for future generations of trainees.

Emergency medicine residents deal with shift work as an added stressor while trying to maintain a healthy work-life balance. Shift work results in many negative health effects including decreased sleep and an increased risk of mental health disorders and illness. EM residents must also rotate through different specialties, some of which are more intensive and time consuming than others and can negatively affect mood. The goal of this study was to evaluate the emotional wellbeing of EM residents during different shift times and rotations. This was an IRB-approved, prospective, observational study wherein EM residents logged information including mood (rated -10 to 10) and current rotation for up to 6 months. 25/34 eligible EM residents completed some of their logs and 14/34 completed their entire log with decreased participation attributed to the COVID-19 pandemic. These logs were blinded to researchers. Emergency department shifts were 8-10 hours long and coded as either day (starting between 6a-10a), evening (starting between 11a-4p), night shift (starting between 9p-12a), post-night (day after night shift), or off. Rotations were grouped into ED, ICU/trauma, and elective/off-service. In order to isolate the effect of rotation on mood while at work, vacation and days off were excluded from analysis when comparing rotations. One way ANOVA test was used to compare moods. Afterwards, post hoc analysis using a Bonferroni Correction was performed to determine between group differences. There were statistically significant differences found between the moods based on shift, F(4, 1888) = 39.1, p = 1.82E-31). Mood was highest during days off (M=5.19), and lowest on night shifts (M=2.36). There were statistically significant differences when comparing night (M=2.36, SD=4.11) vs post-night (M=4.24, SD=3.58), p=0.0007 and day (M=3.60, SD=4.09), p = 0.0015. There were also significant differences between off (M=5.19, SD=3.27), vs day (M=3.60, SD=4.09), p = 2.24E-10, evening (M=2.92, SD=4.46), p=3.01E-26), and night (M=2.36, SD=4.11), p=2.24E-23). There were also statistically significant differences between the moods based on rotation (F(2,1764) = 26.08, p = <.001). Mood was highest on elective/off-service rotations (M=4.63, SD=3.00) and lowest on ICU/Trauma (M=2.36, SD=3.97). There were statistically significant differences comparing mood while in the ED (M=3.00, SD=4.32) vs ICU/trauma (p=.014) and elective/off-service (p=<.001), and between ICU/trauma vs elective/off- service (p=<.001). Night shift and ICU/trauma months resulted in the lowest moods reported by EM residents, while days off and elective/off-service rotations resulted in the highest mood. Shift work remains a significant stressor on EM residents' work-life balance. Additionally, the more intensive rotations can negatively affect residents' moods, further worsening work-life balance. Future studies can investigate methods to improve mood on night shifts and rotations associated with decreased moods.

Exercise and physical fitness have shown to decrease cardiovascular disease, numerous chronic medical conditions and improve mental health. Unfortunately, physical fitness declines during medical residency training. We sought to quantify and characterize the activity of emergency medicine (EM) residents. This study was an IRB approved, prospective, observational study that assessed EM residents' objective activity data obtained from a Fitbit. Thirty-four EM residents were asked to participate in the study and 33 (23 male, 10 female) gave verbal consent. A Fitbit (Charge 3 device) was given to each resident and they were asked to wear the Fitbit at all times. The study was conducted over a six-month period (December 2019-May 2020), but some residents stopped early due to the COVID-19 pandemic. The data from each Fitbit was automatically synced to a secure database, Fitabase. Each resident's identity was hidden from investigators. Data was interpreted using descriptive statistics, taking the mean steps, calories or logged activity time for the group. Mean times overall for various shifts were compared using one way ANOVA tests. Posttest two-way T tests using Bonferroni calculations were used to detect between groups differences. Data specifically collected included FitBit number of steps taken and calories measured and resident logged physical activity. The mean number of steps taken was 8002-8522 over the various shifts. No significant differences were found between shifts. One-way ANOVA was not significant, F(4, 2550) =1.43, p=0.22. The mean number of calories burned in a day was 2661-2869. Residents used the most calories working day shift (M=2869) followed by evening shift (M=2827). One-way ANOVA was significant, F(4, 2550) =11.7, p=1.99E-09. Calories used working the day shift (M=2869, SD= 549) were significantly higher than the night shift (M=2716, SD= 598), p=0.0012 or having a shift off (M=2661, SD= 632), p=3.93E-08. A significant difference was also found in evening shift (M=2827, SD= 670) compared to a shift off (M=2661, SD= 632), p=5.91E-08. Logged physical activity was 13-26 minutes per day. One-way ANOVA was significant, F(4, 1696) =7.87, p=2.77E-06. We found a significantly higher amount of mean logged activity for residents having a shift off (M=26, SD= 39) compared to day shift (M=13, SD= 30), p=3.02E-06 and evening shift (M=19, SD= 31), p=0.0005. There was no significant difference between having a shift off and night shift (M=19, SD= 33), p=0.03 (Bonferroni correction <0.005 for 10 comparisons) and post call (M=17, SD= 30), p=0.06. Although the number of steps did not significantly vary amount various shift, residents did use more calories working the day and evening shifts, which are both typically known to be the busier shifts. Residents who had no shift scheduled, logged more time for dedicated physical activity compared to residents working a shift. With physical activity declining during residency, it is imperative that residents receive education on how to include exercise in their daily routine, especially before or after shifts.

Study Objectives: Sleep is crucial for the optimal function of all human beings, physicians included. As administrations have begun to focus on the well-being of healthcare workers, more studies are needed to determine how current working patterns affect emotional and physical well-being of these workers. It is therefore important to understand the effects of the demands of residency training on sleep quality. The goal of this study was to better evaluate the characteristics of junior physicians' sleep and to determine how shift work impacts sleep quality in these doctors. Methods: This was an IRB-approved, prospective, observational study in which EM residents had sleep characteristics (sleep time, light, deep, and REM sleep) recorded by a Fitbit© device (Charge 3). 34 emergency medicine residents were consented and had their sleep data sent to a secure database, Fitabase ©. Data was recorded from December 2019 to May 2021 for six months total, although some residents opted out of the study early due to the COVID-19 pandemic. Emergency department shifts were 8-10 hours long and coded as either day (starting between 6a-10a), evening (starting between 11a-4p), night shift (starting between 9p-12a), post-night (day after night shift), or off. One way ANOVA test was used to compare sleep times, with Bonferroni Correction post hoc analysis performed to determine between group differences. There were statistically significant differences found between the sleep times based on shift. ANOVA for total time in bed F(4, 2067) = 44.9 p = 3.30E-36, total sleep F(4, 2067) = 43.8, p = 2.34E-35, time spent awake during sleep F(4, 2067) = 20.8, p = 7.34E-17, time spent in light sleep F(4, 2067) = 39.1, p = 1.32E-31, and time spent in deep sleep F(4, 2067) = 9.78, p = 7.69E-08 and finally REM sleep F(4, 2067) = 19.2 p = 1.58E-15 were all significant. Between group differences were found for night shift as compared to all other shifts, meaning less time (minutes) was spent in bed after night shifts (M=364). There was less total (M=320), light (M=187), deep (M=65) and REM sleep (M=68) following night shifts as compared to all other shared. (T test statistics significant but not shared for sake of brevity). In addition, there was significantly less total sleep for evening shifts (M=389), as compared to days off (M=408), p = 7.19E-05 and day shifts (M=417), p = 6.41E-07. Evening shifts also had less light sleep (M=229) as compared to day shift (M=243), p = 0.0009 and shifts off (M=247), p = 8.82E-08. Results also showed less REM sleep found between evening shifts (M=84) and day shifts (M=95), p = 1.28E-05. Night shifts and evening shifts resulted in statistically significant reduced sleep recorded on Fitbit© devices as compared to shifts off and day shifts. This is important to note because emergency physicians will spend a significant amount of their careers performing night or evening shifts. Thus it is critical for emergency medicine physicans to be aware of these sleeep changes and to adjust their sleep habits (naps before night shifts, installing blackout blinds, etc.) to ensure that they get optimal sleep amounts.

Shift work is one of the many stressors that emergency physicians need to solve as they strive to find a proper work-life balance. One of the way that shift workers combat shift work is by using caffeine or other stimulants. Finding the right balance means that shift workers must adjust their sleep schedule often. The goal of this study was to evaluate the amount of sleep that EM residents believe they're getting based on shift times. This was an IRB-approved, prospective, observational study wherein EM residents wrote in individual sleep logs their sleep time, caffeine usage, and sleep aids for up to 6 months. 25/34 eligible EM residents completed some of their sleep logs and 14/34 completed their entire sleep log with decreased participation attributed to COVID-19. These logs were blinded to researchers and then compared based on shift (day, evening, night, off, post-nights). Shifts were 8-10 hours long and coded as either day (starting between 6a-10a), evening (starting between 11a-4p), night shift (starting between 9p-12a), post-night (day after night shift), or off. One way ANOVA tests were used to compare sleep and caffeine recorded by shift. Post hoc analysis using Bonferroni correction were performed to determine differences between groups. There were statistical differences between the sleep recorded and caffeine use by emergency medicine residents based on shift. Sleep time was highest during days off while caffeine usage was the lowest. On the contrary, sleep time was lowest after night shifts while caffeine usage was the highest. One way ANOVA for sleep time based on shifts was F (4, 1884)=15.61, p=1.45E-12. There was a significant difference between sleep time on days off (M=464.9, SD=106.6) compared to day shift (M=443.8, SD=90.1), p=0.004, evening shift (M=444.8, SD=89.1), p=0.0003, and night shift (M=398.8, SD=122.2, p=1.92E-13. There were also significant differences between day shift and night shift (p=8.14E-06) and evening shift and night shift (p=3.41E-08). The one way ANOVA for caffeine usage amongst shifts was significant at F (4, 1626)=11.91, p=1.55E-09. Statical differences were found between days off (M=158.0, SD=126.7) as compared to evening shifts (M=200.3, SD=138.3), p=5.49E-08 and also night shifts (M=220.9, SD=145.5), p=3.86E-08. Night shifts resulted in the lowest sleep and highest caffeine usage for emergency medicine residents. Days off and day shifts resulted in highest amount of sleep and lowest amounts of caffeine usage. Shift work remains a significant stressor on emergency residents' work-life balance. Residents should attempt to make adjustments to their sleeping arrangements at home to improve their night shift sleep.

Introduction: Many learners use the internet or other independent means as a primary way to master procedures. There are also numerous described methods to teach procedures using simulation. The optimal method for teaching procedures is unknown. We compare residents' confidence and performance of pediatric airway skills (bag valve mask [BVM] and endotracheal intubation [ETI]) and their confidence in teaching these skills to others after training using (1) standard simulation (SS), (2) the Peyton method, or (3) self-directed learning. Materials and Methods: In 2019–2020, emergency medicine (EM) residents at a single program were randomized to one of three training groups. Prior to training, residents underwent standard airway simulation skill assessment sessions with two blinded observers. Residents in the SS group then underwent training using SS with postprocedure debriefing. Residents in the Peyton method group underwent simulation through a structured technique described elsewhere. The residents in the independent learning group were encouraged to master the skills through any means they saw fit. Residents were surveyed regarding prior experience, knowledge base, and confidence in performing and teaching procedures. Results: Thirty-three residents were randomized. After training, there were no differences between groups in comfort performing procedures. Residents randomized to independent learning were less comfortable teaching ETI than other groups. In 4–6 month follow-up, all residents showed improvement in procedural performance, regardless of assigned learner group. Conclusions: Residents using self-directed learning to master airway skills are less comfortable teaching ETI than those taught using simulation. Their skill performance is equivalent regardless of teaching method. The following core competencies are addressed in this article: Medical knowledge, Patient care, Practice-based learning and improvement, Systems-based practice.

The optimal method for teaching procedures is not known. For uncommonly performed procedures such as pediatric airway procedures, practical learning is often supplemented with simulation (sim) or digital platforms. In this study, we compare residents' performance of pediatric bag valve mask (BVM) and endotracheal intubation (ETI) after undergoing training using (1) Standard sim, (2) The Peyton method, or (3) Self-directed learning with free access to sim mannikins.

Sleep is an integral part of both physical and mental well-being, and has been long revered as a delicacy during medical residency training. We sought to quantify and characterize the sleep of emergency medicine (EM) residents training at a level one trauma center in eastern PA. This study was an IRB-approved, prospective, observational study that assessed EM residents' objective sleep data obtained from a Fitbit. EM residents gave consent to participate in the study. A Fitbit Charge 3 device was given to each resident and they were asked to wear the Fitbit at all times, except for a brief period required for weekly charging of the device. The Fitbit automatically tracks time in bed, total sleep time, and time spent in light, deep and REM sleep. The study was conducted over a three-month period. The data from each Fitbit was automatically synced to a database, Fitabase. Each resident was identified by a subject number and their identity was hidden from investigators. Data was interpreted using descriptive statistics, first taking the median time for each subject and then the mean for the group. Median times overall for female and male subjects were compared using a two-tailed t-test. Fitbit sleep data were collected for 33 EM residents over a three month period, 10 female and 23 male. The average median sleep time per night was 423.1 minutes (7.1 hours), 450.1 for females and 411.4 for males (P=.0063). In total, 18 participants had a median nightly sleep time of less than 7 hours (54.5%), only 2 subjects had a median sleep time of less than 6 hours (6.1%), and there were no subjects with a median sleep time of less than 5 hours. The average median REM time nightly was 85.7 minutes, 93.3 for females and 82.3 for males (P=.087). The average median time spent in bed while awake was 56.5 minutes, 62.9 for females and 53.7 for males (P=.0064). This value represents the total latency, defined as the time it takes the subject to fall asleep as well time spent awake between sleep phases. In that regard, it may be used as a marker for poor sleep quality or difficulty initiating sleep. Although average median sleep time in our EM residents is 7.1 hours, more than half of our residents had a median sleep time of less than 7 hours nightly. Comparatively, only 35.2% of adults in the United States sleep less than 7 hours nightly per 2014 CDC data. Females had statistically significant higher average median sleep times and total sleep latency. EM residents in particular are at risk for poor sleep hygiene given the predominance of shift work, which may be one reason many of our residents had a median sleep time of less than 7 hours nightly. For residents especially, paying attention to sleep amount and adequacy may be an important wellness tool to promote both physical and mental health.

Study Objective: We evaluate the validity of our internal resident applicant interview scoring system with actual resident performance after at least 2 years of training. We also compare our internal scoring system with the Standardized Video Interview (SVI) scores obtained by the Electronic Residency Application Service. Materials and Methods: The first phase of our study was a before-and-after cohort of six consecutive classes from a single emergency medicine residency program. Faculty members were blinded to each resident's interview score before starting residency and asked to assess their current performance on the same scoring system. The second phase of the study was a prospective cohort of 124 emergency medicine residency candidates interviewing during the 2017–2018 cycle. Results: Fifty-one residents at the postgraduate year 2 level or higher had scoring data available from their interviews and participated in the before-and-after phase of this study. Their mean interview score before starting their residencies was 69.2 on a 100-mm Visual Analog Scale (VAS), with a range of 38.5–94.3. Their performance VAS score after at least 2 years of training had a mean of 69.7 (standard deviation: 15.8), with a range of 13.2–90.1. Using Wilcoxon ranked-sum testing for repeated measures, there were no differences (P = 0.95). Only four residents' VAS scores dropped more than 2 cm. The second phase included a cohort of 124 total applicants from the 2017 to 2018 cycle. Applicant VAS scores ranged from 5 to 91.7 mm, with a mean of 60 mm. Their SVI scores ranged from 13 to 27, with an average of 19.4. The values had a weakly negative relationship, with a correlation coefficient of −0.1. Conclusions: Traditional interviews are a relatively accurate predictor of individual resident performance. There is no correlation between traditional interviews and SVI scores. Although the SVI was initiated to help demonstrate an applicant's interpersonal and communication skills, a face-to-face conversation is irreplaceable. The following core competency statement: Systems-based practice.

Race and sex disparities in health care have been previously documented in the literature. A contributing factor may be "unconscious bias" - the concept that patients may be treated differently due to social stereotypes a provider is unaware they are acting upon. This effect may be more pronounced in busy and stressful environments such as the emergency department. Socioeconomic status and other social determinants of health likely also contribute to disparities in care. Our study objective was to describe patterns in emergency care surrounding race and sex demographics. Subjective measurements included patient satisfaction surveys rating provider empathy and quality of visit. Objective measurements included admission rates and length of stay (LOS). A descriptive secondary analysis of prospective data collected at a tertiary academic level 1 trauma center emergency department was performed from July to August 2018. All comers were included. A non-physician research assistant asked the patient or family member to complete a survey rating physicians on courtesy, listening, concern for comfort, informed on care, treatment of pain, time waiting, and overall visit. Patient demographics, length of stay, and patient disposition were recorded. 204 patients responded overall. Median satisfaction scores in nearly all categories ranged from 4 (good) to 5 (very good). Median White LOS was 192 minutes vs non-white LOS of 185.5 minutes, Black LOS was 207 minutes. White discharge rate was 47.9% vs 75.6% non-White overall, Black discharge rate was 80%, and Hispanic discharge rate was 74.1%. Male discharge rate was 60.7% vs 58.3% female. Complete Median LOS and % admission rate by race and sex are reported in Table 1. Patient satisfaction scores were comparable across both race and sex. Median LOS and discharge rate by sex was comparable. LOS for Black demographic patients was 15 minutes longer than White patients and 21.5 minutes longer than non-White patients. This may be meaningful particularly given a high discharge rate of 80% for Black patients. Non-White patients overall had a much higher discharge rate from the emergency department compared to White patients. Possible factors for this large difference include lack of insurance, access to primary care, health literacy, and socioeconomic status. Another concerning possibility is that unconscious bias may result in providers downplaying the severity of patients' symptoms due to racial differences. This study is limited by population demographics specific to this single center. Further investigation is warranted to differentiate the cause of admission rate variance, and how this may impact patient outcomes.Table 1Length of Stay (median, range)Disposition (n, %)White (n = 121)192 (32 - 646)Discharged: 58 (47.9%) Admit Floor: 61 (50.4%) Admit ICU: 2 (1.7%)Black (n = 25)207 (0 - 1053)Discharged: 20 (80%) Admit Floor: 4 (16%) Admit ICU: 1 (4%)Hispanic (n = 54)183 (3 - 582)Discharged: 40 (74.1%) Admit Floor: 13 (24.1%) Admit ICU: 1 (1.9%)Asian (n = 1)88Discharged: 1 (100%)Other (n = 2)223.5 (174 - 273)Discharged: 1 (50%) Admit Floor: 1 (50%)Non-White (all) (n = 82)185.5 (0 - 1053)Discharged: 62 (75.6%) Admit Floor: 18 (22%) Admit ICU: 2 (2.4%)Male (n = 89)189 (31 - 1053)Discharged: 54 (60.7%) Admit Floor: 34.8%) Admit ICU: 2 (2.2%) Transferred: 2 (2.2%)Female (n = 115)193 (0 - 582)Discharged: 67 (58.3%) Admit Floor: 48 (41.7%) Open table in a new tab

OBJECTIVE:To investigate (1) cardiopulmonary resuscitation (CPR) adequacy during simulated cardiac arrest of equipped football players and (2) whether protective football equipment impedes CPR performance measures.DESIGN:Exploratory crossover study performed on Laerdal SimMan 3 G interactive manikin simulator.SETTING:Temple University/St Luke's University Health Network Regional Medical School Simulation Laboratory.PARTICIPANTS:Thirty BCLS-certified ATCs and 6 ACLS-certified emergency department technicians.INTERVENTIONS:Subjects were given standardized rescuer scenarios to perform three 2-minute sequences of compression-only CPR. Baseline CPR sequences were captured on each subject.MAIN OUTCOME MEASURES:Experimental conditions included 2-minute sequences of CPR either over protective football shoulder pads or under unlaced pads. Subjects were instructed to adhere to 2010 American Heart Association guidelines (initiation of compressions alone at 100/min to 51 mm). Dependent variables included average compression depth, average compression rate, percentage of time chest wall recoiled, and percentage of hands-on contact during compressions.RESULTS:Differences between subject groups were not found to be statistically significant, so groups were combined (n = 36) for analysis of CPR compression adequacy. Compression depth was deeper under shoulder pads than over (P = 0.02), with mean depths of 36.50 and 31.50 mm, respectively. No significant difference was found with compression rate or chest wall recoil.CONCLUSIONS:Chest compression depth is significantly decreased when performed over shoulder pads, while there is no apparent effect on rate or chest wall recoil. Although the clinical outcomes from our observed 15% difference in compression depth are uncertain, chest compression under the pads significantly increases the depth of compressions and more closely approaches American Heart Association guidelines for chest compression depth in cardiac arrest.

BACKGROUND:Although the issues concerning the impact of emergency department (ED) overcrowding have been the subject of much recent concern, there are few data regarding the effect of ED census on emergency physician behavior with respect to the decision to admit patients. Admission rates might either increase or decrease on busy days, when the system and the physician are under stress. STUDY OBJECTIVE:The purpose of this study was to determine if ED physicians change their admitting behavior depending on ED census. METHODS:This was a retrospective review of 3 months' data (92 consecutive days, July 9-October 9, 2006) in a community ED with an annual census of approximately 70,000 patients and an emergency medicine residency program. We defined each of the 92 days to be either "busy" (> 180 patients seen), "slow" (< 147 patients seen) or "medium" (147-180 patients seen). We then compared the rates of admission to the hospital on the "busy," "medium," and "slow" days. We also compared each attending physician's personal rates of admission on slow days to his or her rate of admission on medium or busy days. ED staffing was constant throughout the study period. All comparisons were with chi-squared. RESULTS:There were 14,969 patients seen in the ED during the 92 study days. On "busy" days, 20.1% of the 3400 patients were admitted to the hospital; on "medium" days, 20.6% of the 9057 patients were admitted; on "slow" days, 19.7% of the 2512 patients were admitted. There was no significant association between the level of patient volume in the ED and rate of admission (p = 0.55). When comparing each of 14 attending physicians to him- or herself, there was no significant association found between rate of admission and ED census (all p values > 0.3). All three categories of days, "busy," "medium," and "slow" did not differ in terms of acuity as judged by triage level distribution. CONCLUSION:The likelihood of a patient's admission vs. discharge is not affected by ED patient volume. Furthermore, we found no evidence that an individual physician's admitting behavior was associated with ED patient volume.

OBJECTIVES:This exploratory study compared the screening ability of a newly introduced radiation detection portal with a traditional Geiger counter for detection of radiation contamination in the setting of a mass casualty training exercise.METHODS:Following a pretrial evaluation of interobserver reliability for Geiger counter use, 30 volunteers were randomly assigned to don gowns containing three disks, each of which was either a sham resembling the radioactive samples or an actual cesium-137 sample; each subject participated a minimum of four times with different gowns each time. Each subject underwent standard radioactivity screening with the Geiger counter and the portal.RESULTS:Interobserver reliability was excellent between the two Geiger counter screeners in the pretrial exercise, correctly identifying 101 of 102 sham and radioactive samples (κ = 0.98; 95% confidence interval [CI] = 0.94 to 1.00). For radioactively labeled subjects across all bodily locations, the portal (43/61, or 70.5%; 95% CI = 58.1% to 80.5%) was less sensitive than the Geiger counter screening (61/61, or 100%; 95% CI = 92.9% to 100%), which resulted in a portal false-negative rate of 29.5%. For radiation detection in the posterior thorax, the portal radiation screening (4/19, or 21.1%; 95% CI = 8% to 43.9%) was less accurate than the Geiger counter (19/19, or 100%; 95% CI 80.2% to 100%). In contrast, there were no major differences between the portal and the Geiger counter for radiation detection at the left shoulder, right shoulder, or sham (nonradiation) detection. There were no false-positive detections of the sham-labeled subjects for either device, yielding a specificity of 100% for both screening modalities.CONCLUSIONS:Geiger counter screening was more sensitive than, and equally specific to, radiation detection portal screening in detecting radioactively labeled subjects during a radiation mass casualty drill.

To determine if a formal departmental drug seeker policy and an intranet drug seeker database would significantly decrease the number of narcotics dispensed to patients deemed to be drug seekers.

A 28-year-old man with a history of drug and alcohol abuse presented multiple times to the hospital over 2 months with an elusive constellation of symptoms, resolving spontaneously in each instance. This patient required a high level of care for management and stabilization, including 3 emergency department visits, 2 medical floor admissions, and 1 intensive care unit admission. In both the emergency department and inpatient setting, all laboratory and imaging study results, including gas chromatography/mass spectrophotometry of the urine, were negative/normal. A definitive diagnosis eluded multiple emergency medicine, critical care, and consulting physicians. His symptoms included altered mental status, vomiting, diaphoresis, and mydriasis. The patient later admitted using mushrooms to a nurse. In the absence of confirmatory testing, but supported by exclusionary and anecdotal data, we believe that our patient's symptoms are consistent with Psilocybe mushroom toxicity. We feel that had this been considered initially, the correct diagnosis would have led to a better utilization of resources, and we want to remind emergency physicians of the possibility of mushroom abuse in any similar clinical setting.

In September 2003, Pennsylvania weakened its motorcycle helmet law. As a result, only inexperienced riders and those under the age of 21 are required to wear helmets. We analyzed data to determine if fatalities and severity of injuries in motorcycle accidents have changed since the weakening of this law.