Israel Medical Association Journal

Background: Data regarding the management of adnexal torsion (AT) during the coronavirus disease 2019 (COVID-19) pandemic are scarce.

Objectives: To study the effects of actions to limit the spread of COVID-19 on AT management.

Methods: We conducted a retrospective cohort study of all women who underwent laparoscopy for suspected AT between March 2011 and February 2021. We compared the COVID-19 pandemic period, (15 March 2020–2 August 2021, group A) to a parallel period (2019–2020, group B), and a 9-year period preceding the pandemic (March 2011–February 2020, group C).

Results: We performed 97 laparoscopies in group A, 82 in group B, and 635 in group C. The proportion of women presenting following in vitro fertilization treatment was lower (odds ratio [OR] 0.22, 95% confidence interval ]95%CI] (0.06–0.86), P < 0.023). Time from admission to decision to operate was shorter (2.7 vs. 3.9 hours, P = 0.028) in group A than group B. Time from admission to surgery was shorter (9.1 vs. 12.5 hours, P = 0.005) and the rate of surgically confirmed AT was lower (59 [60.8%] vs. 455 [71.7%], P = 0.030, OR 0.61, 95%CI 0.39–0.95) in group A than group C. Among surgically confirmed AT cases only, mean time from admission to decision was shorter in group A than group B (2.6 vs. 4.6 hours, P = 0.014).

Conclusions: We identified differences in time from admission to clinical decision and from admission to surgery among women with suspected AT during the COVID-19 pandemic.

Background: Completing internal medicine specialty training in Israel involves passing the Israel National Internal Medicine Exam (Shlav Aleph), a challenging multiple-choice test. multiple-choice test. Chat generative pre-trained transformer (ChatGPT) 3.5, a language model, is increasingly used for exam preparation.

Objectives: To assess the ability of ChatGPT 3.5 to pass the Israel National Internal Medicine Exam in Hebrew.

Methods: Using the 2023 Shlav Aleph exam questions, ChatGPT received prompts in Hebrew. Textual questions were analyzed after the appeal, comparing its answers to the official key.

Results: ChatGPT 3.5 correctly answered 36.6% of the 133 analyzed questions, with consistent performance across topics, except for challenges in nephrology and biostatistics.

Conclusions: While ChatGPT 3.5 has excelled in English medical exams, its performance in the Hebrew Shlav Aleph was suboptimal. Factors include limited training data in Hebrew, translation complexities, and unique language structures. Further investigation is essential for its effective adaptation to Hebrew medical exam preparation.