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The purpose of this study was to investigate whether the "bystander effect" known to occur in emergency situations is effective in bullying situations through examination of the individual experiences of 467 middle- and high-school students. While the bystander effect was not found to be valid in bullying situations, there were significant differences in factors influencing a bystander's defending behavior in terms of the presence of other bystanders. In cases where bullying was witnessed in the presence of other bystanders, factors such as one's previous experience as a perpetrator, anti-social behavior, level of harm, relationship to the victim and popularity had an effect on the defending behavior of bystanders. On the other hand, in the absence of other bystanders, one's previous experience as a victim, level of empathy, and perceived control had an effect on the defending behavior of bystanders. The implications of these results for an effective intervention for school bullying in terms of bystanders are discussed.

Introduction: To balance adequate pain management while minimizing opioid-related harms after surgery, opioid prescribing guidelines rely on patient-reported use after surgery. However, it is unclear how many patients are required to develop precise guidelines. We aimed to compare patterns of use, required sample size, and the precision for patient-reported opioid consumption after common surgical procedures., Methods: We analyzed procedure-specific 30-day opioid consumption data reported after discharge from 15 common surgical procedures between January 2018 and May 2019 across 65 hospitals in the Michigan Surgical Quality Collaborative. We calculated proportions of patients using no pills and the estimated number of pills meeting most patients' needs, defined as the 75th percentile of consumption. We compared several methods to model consumption patterns. Using the best method (Tweedie), we calculated sample sizes required to identify opioid consumption within a 5-pill interval and estimates of pills to meet most patients' needs by calculating the width of 95% CIs., Results: In a cohort of 10,688 patients, many patients did not consume any opioids after all types of procedures (range 20%-40%). Most patients' needs were met with 4 pills (thyroidectomy) to 13 pills (abdominal hysterectomy). Sample sizes required to estimate opioid consumption within a 5-pill wide 95% CI ranged from 48 for laparoscopic appendectomy to 188 for open colectomy. The 95% CI width for estimates ranged from 0.7 pills for laparoscopic cholecystectomy to 7.0 pills for ileostomy/colostomy., Conclusions: This study demonstrates that profiles of opioid consumption share more similarities than differences for certain surgical procedures. Future investigations on patient-reported consumption are required for procedures not currently included in prescribing guidelines to ensure surgeons and perioperative providers can appropriately tailor recommendations to the postoperative needs of patients., Competing Interests: Competing interests: JS, YL, and VG have no disclosures. CMB, MJE, JFW, and MCB receive funding from the Michigan Department of Health and Human Services and the National Institute on Drug Abuse (R01DA042859). CMB is a consultant for Heron Therapeutics, Vertex Pharmaceuticals, Alosa Health and the Benter Foundation, not related to this work. JS had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis., (© American Society of Regional Anesthesia & Pain Medicine 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts., Competing Interests: Disclosure Statement. The authors declare no conflicts of interest.