Near-death experiences after cardiac arrest: a scoping review.

BACKGROUND: This scoping review aimed to characterise near-death experiences in the setting of cardiac arrest, a phenomenon that is poorly understood and may have clinical consequences. METHOD: PubMed/MEDLINE was searched to 23 July 2023 for prospective studies describing near-death experiences in cardiac arrest. PRISMA-ScR guidelines were adhered to. Qualitative and quantitative data were synthesised. Meta-analysis was precluded due to data heterogeneity. RESULTS: 60 records were identified, of which 11 studies involving interviews were included from various countries. Sample size ranged from 28-344, and proportion of female patients (when reported) was 0-50%, with mean age (when reported) ranging 54-64 years. Comorbidities and reasons for cardiac arrest were heterogeneously reported. Incidence of near-death experiences in the included studies varied from 6.3% to 39.3%; with variation between in-hospital (6.3-39.3%) versus out-of-hospital (18.9-21.2%) cardiac arrest. Individual variables regarding patient characteristics demonstrated statistically significant association with propensity for near-death experiences. Reported content of near-death experiences tended to reflect the language of the questionnaires used, rather than the true language used by individual study participants. Three studies conducted follow-up, and all suggested a positive life attitude change, however one found significantly higher 30-day all-cause mortality in patients with near-death experiences versus those without, in non-controlled analysis. CONCLUSIONS: From prospective studies that have investigated the phenomenon, near-death experiences may occur in as frequent as over one-third of patients with cardiac arrest. Lasting effects may follow these events, however these could also be confounded by clinical characteristics.

Professionalism and clinical short answer question marking with machine learning.

Machine learning may assist in medical student evaluation. This study involved scoring short answer questions administered at three centres. Bidirectional encoder representations from transformers were particularly effective for professionalism question scoring (accuracy ranging from 41.6% to 92.5%). In the scoring of 3-mark professionalism questions, as compared with clinical questions, machine learning had a lower classification accuracy (P < 0.05). The role of machine learning in medical professionalism evaluation warrants further investigation.

Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional study.

To utilise effectively tools that employ machine learning (ML) in clinical practice medical students and doctors will require a degree of understanding of ML models. To evaluate current levels of understanding, a formative examination and survey was conducted across three centres in Australia, New Zealand and the United States. Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty with identifying weaknesses in model performance analysis. Further studies examining educational interventions addressing such ML topics are warranted.

Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review.

OBJECTIVES: With the high and rising total cost of medical school, medical student debt is an increasing concern for medical students and graduates, with significant potential to impact the well-being of physicians and their patients. We hypothesised that medical student debt levels would be negatively correlated with mental health and academic performance, and would influence career direction (ie, medical specialty choice). DESIGN: We performed a systematic literature review to identify articles that assessed associations between medical student mental health, academic performance, specialty choice and debt. The databases PubMed, Medline, Embase, Scopus and PsycINFO were searched on 12 April 2017, for combinations of the medical subject headings Medical Student and Debt as search terms. Updates were incorporated on 24 April 2019. RESULTS: 678 articles were identified, of which 52 met the inclusion criteria after being reviewed in full text. The majority of studies were conducted in the USA with some from Canada, New Zealand, Scotland and Australia. The most heavily researched aspect was the association between medical student debt and specialty choice, with the majority of studies finding that medical student debt was associated with pursuit of higher paying specialties. In addition, reported levels of financial stress were high among medical students, and correlated with debt. Finally, debt was also shown to be associated with poorer academic performance. CONCLUSIONS: Medical student debt levels are negatively associated with mental well-being and academic outcomes, and high debt is likely to drive students towards choosing higher paying specialties. Additional prospective studies may be warranted, to better understand how educational debt loads are affecting the well-being, career preparation and career choices of physicians-in-training, which may in turn impact the quality of care provided to their current and future patients.