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To diagnose LACNES, Maatman et al1 suggested that three of the following criteria should be met: (1) >3 months history of locoregional flank pain; (2) a fingertip-sized area of constant tenderness in the flank along the midaxillary line, with pressure eliciting high-intensity pain; (3) altered skin sensations—such as hypoesthesia, hyperesthesia or altered cold perception—in surrounding areas; and (4) negative pinch test results surrounding the tender spot. In a retrospective analysis of 30 patients with LACNES (70% women; median age, 52 years; range, 13–78 years) who met all the criteria of Maatman et al, the median time from onset to diagnosis was >18 months. [...]their effectiveness is unconfirmed.1 3 Despite recent advances in imaging technology, LACNES is considered an underdiagnosed condition.1 4 5 To establish a definitive diagnosis in patients with unidentifiable pain, physicians must carefully investigate pain duration, extent of the nociceptive dermatome, presence or absence of a pinch sign and circumstances in which pain is induced while considering differential diagnosis as shown in Box 1.Box 1 Differential diagnosis of LACNES LACNES-like pain Abdominal myofascial pain syndrome Abdominal wall (haematoma, endometriosis, tumour, tear) Radiculopathy (diabetic, traumatic) Scar tissue Slipping rib syndrome Rib abnormalities Postherpetic neuralgia Neurofibroma Schwannoma Herniated disk LACNES, lateral cutaneous nerve entrapment syndrome.

Qualitative research is crucial for understanding the values and beliefs underlying individual experiences, emotions, and behaviors, particularly in social sciences and health care. Traditionally reliant on manual analysis by experienced researchers, this methodology requires significant time and effort. The advent of artificial intelligence (AI) technology, especially large language models such as ChatGPT (OpenAI), holds promise for enhancing qualitative data analysis. However, existing studies have predominantly focused on AI's application to English-language datasets, leaving its applicability to non-English languages, particularly structurally and contextually complex languages such as Japanese, insufficiently explored. This study aims to evaluate the feasibility, strengths, and limitations of ChatGPT-4 in analyzing qualitative Japanese interview data by directly comparing its performance with that of experienced human researchers. A comparative qualitative study was conducted to assess the performance of ChatGPT-4 and human researchers in analyzing transcribed Japanese semistructured interviews. The analysis focused on thematic agreement rates, interpretative depth, and ChatGPT's ability to process culturally nuanced concepts, particularly for descriptive and socio-culturally embedded themes. This study analyzed transcripts from 30 semistructured interviews conducted between February and March 2024 in an urban community hospital (Hospital A) and a rural university hospital (Hospital B) in Japan. Interviews centered on the theme of \"sacred moments\" and involved health care providers and patients. Transcripts were digitized using NVivo (version 14; Lumivero) and analyzed using ChatGPT-4 with iterative prompts for thematic analysis. The results were compared with a reflexive thematic analysis performed by human researchers. Furthermore, to assess the adaptability and consistency of ChatGPT in qualitative analysis, Charmaz's grounded theory and Pope's five-step framework approach were applied. ChatGPT-4 demonstrated high thematic agreement rates (>80%) with human researchers for descriptive themes such as \"personal experience of a sacred moment\" and \"building relationships.\" However, its performance declined for themes requiring deeper cultural and emotional interpretation, such as \"difficult to answer, no experience of sacred moments\" and \"fate.\" For these themes, agreement rates were approximately 30%, revealing significant limitations in ChatGPT's ability to process context-dependent linguistic structures and implicit emotional expressions in Japanese. ChatGPT-4 demonstrates potential as an auxiliary tool in qualitative research, particularly for efficiently identifying descriptive themes within Japanese-language datasets. However, its limited capacity to interpret cultural and emotional nuances highlights the continued necessity of human expertise in qualitative analysis. These findings emphasize the complementary role of AI-assisted qualitative research and underscore the importance of further advancements in AI models tailored to non-English linguistic and cultural contexts. Future research should explore strategies to enhance AI's interpretability, expand multilingual training datasets, and assess the applicability of emerging AI models in diverse cultural settings. In addition, ethical and legal considerations in AI-driven qualitative analysis require continued scrutiny.

Classification and analysis of existing data on medical malpractice lawsuits are useful in identifying the root causes of medical errors and considering measures to prevent recurrence. No study has shown the actual prevalence of all closed malpractice claims in Japan, including the number of cases and their trial results. In this study, we illustrated the recent trends of closed malpractice claims by medical specialty, the effects of the acceptance rates and the settlements and clarified the trends and characteristics. This was a descriptive study of all closed malpractice claims data from the Supreme Court in Japan from 2006–2021. Trends and the characteristics in closed malpractice claims by medical specialty and the outcomes of the claims, including settlements and judgments, were extracted. The total number of closed medical malpractice claims was 13,340 in 16 years, with a high percentage ending in settlement (7,062, 52.9%), and when concluding in judgment (4,734, 35.3%), the medical profession (3,589, 75.8%) was favored. When compared by medical specialty, plastic surgery and obstetrics/gynecology were more likely resolved by settlement. By contrast, psychiatry cases exhibited a lower likelihood of settlement, and the percentage of cases resulting in unfavorable outcomes for patients was notably high. Furthermore, there has been a decline in the number of closed medical malpractice claims in Japan in recent years compared to the figures observed in 2006. In particular, the number of closed medical malpractice claims in obstetrics/gynecology and the number of closed medical malpractice claims per 1,000 physicians decreased significantly compared to other specialties. In conclusion, half of the closed malpractice claims were settled, and a low percentage of patients won their cases. Closed medical malpractice claims in Japan have declined in most medical specialties since 2006. Additionally, obstetrics/gynecology revealed a significant decrease since introducing the Obstetrics/Gynecology Medical Compensation System in 2009.

Objectives In Japan, the prevalence of irregular menstrual cycles and its association with the frequency of night shifts have scarcely assessed. The present study aimed to evaluate the relationship between irregular menstrual cycles and the frequency of night shifts in Japanese female nurses. Methods We conducted a cross‐sectional web‐based self‐administered questionnaire survey in 2019. An irregular menstrual cycle was defined as a cycle length of ≤21 days or ≥39 days at least a few times over the past year or amenorrhea for at least 3 months. We used Poison regression analysis with a robust error variance to calculate the prevalence ratios adjusted for age, body mass index, hospital size, and the department in which they worked. Results A total of 1249 women were included, and 679 (54.4%) and 195 (15.6%) of them worked under two and three rotating shifts. The prevalence of irregular menstrual cycles was 24.8%, 37.4%, and 35.9% in the no night, two rotating, and three rotating shifts groups, respectively. While the frequency of night shifts had a dose‐responsive relationship with irregular menstrual cycles in the two rotating shifts group, it was not observed in the three rotating shifts group. However, the risk of work getting affected by dysmenorrhea or premenstrual symptoms increased in the three rotating shifts group. Conclusions Over 30% of Japanese female nurses working under night shifts had irregular menstrual cycles. The high frequency of night shifts increased the risk of irregular menstrual cycles and secondary amenorrhea in the two rotating shifts group.

Background Diagnostic errors constitute an important medical safety problem that needs improvement, and their frequency and severity are high in emergency room settings. Previous studies have suggested that diagnostic errors occur in 0.6-12% of first-time patients in the emergency room and that one or more cognitive factors are involved in 96% of these cases. This study aimed to identify the types of cognitive biases experienced by physicians in emergency rooms in Japan. Methods We conducted a questionnaire survey using Nikkei Medical Online (Internet) from January 21 to January 31, 2019. Of the 159,519 physicians registered with Nikkei Medical Online when the survey was administered, those who volunteered their most memorable diagnostic error cases in the emergency room participated in the study. EZR was used for the statistical analyses. Results A total of 387 physicians were included. The most common cognitive biases were overconfidence (22.5%), confirmation (21.2%), availability (12.4%), and anchoring (11.4%). Of the error cases, the top five most common initial diagnoses were upper gastrointestinal disease (22.7%), trauma (14.7%), cardiovascular disease (10.9%), respiratory disease (7.5%), and primary headache (6.5%). The corresponding final diagnoses for these errors were intestinal obstruction or peritonitis (27.3%), overlooked traumas (47.4%), other cardiovascular diseases (66.7%), cardiovascular disease (41.4%), and stroke (80%), respectively. Conclusions A comparison of the initial and final diagnoses of cases with diagnostic errors shows that there were more cases with diagnostic errors caused by overlooking another disease in the same organ or a disease in a closely related organ.

The reliability of GPT-4, a state-of-the-art expansive language model specializing in clinical reasoning and medical knowledge, remains largely unverified across non-English languages. This study aims to compare fundamental clinical competencies between Japanese residents and GPT-4 by using the General Medicine In-Training Examination (GM-ITE). We used the GPT-4 model provided by OpenAI and the GM-ITE examination questions for the years 2020, 2021, and 2022 to conduct a comparative analysis. This analysis focused on evaluating the performance of individuals who were concluding their second year of residency in comparison to that of GPT-4. Given the current abilities of GPT-4, our study included only single-choice exam questions, excluding those involving audio, video, or image data. The assessment included 4 categories: general theory (professionalism and medical interviewing), symptomatology and clinical reasoning, physical examinations and clinical procedures, and specific diseases. Additionally, we categorized the questions into 7 specialty fields and 3 levels of difficulty, which were determined based on residents' correct response rates. Upon examination of 137 GM-ITE questions in Japanese, GPT-4 scores were significantly higher than the mean scores of residents (residents: 55.8%, GPT-4: 70.1%; P<.001). In terms of specific disciplines, GPT-4 scored 23.5 points higher in the \"specific diseases,\" 30.9 points higher in \"obstetrics and gynecology,\" and 26.1 points higher in \"internal medicine.\" In contrast, GPT-4 scores in \"medical interviewing and professionalism,\" \"general practice,\" and \"psychiatry\" were lower than those of the residents, although this discrepancy was not statistically significant. Upon analyzing scores based on question difficulty, GPT-4 scores were 17.2 points lower for easy problems (P=.007) but were 25.4 and 24.4 points higher for normal and difficult problems, respectively (P<.001). In year-on-year comparisons, GPT-4 scores were 21.7 and 21.5 points higher in the 2020 (P=.01) and 2022 (P=.003) examinations, respectively, but only 3.5 points higher in the 2021 examinations (no significant difference). In the Japanese language, GPT-4 also outperformed the average medical residents in the GM-ITE test, originally designed for them. Specifically, GPT-4 demonstrated a tendency to score higher on difficult questions with low resident correct response rates and those demanding a more comprehensive understanding of diseases. However, GPT-4 scored comparatively lower on questions that residents could readily answer, such as those testing attitudes toward patients and professionalism, as well as those necessitating an understanding of context and communication. These findings highlight the strengths and limitations of artificial intelligence applications in medical education and practice.

Although research in general medicine is important, the contributions and characteristics of general medicine physicians (GMPs) in university hospitals (UH) and community healthcare facilities (CHF) remains unclear. Therefore, this study examines the popularity of research by affiliation, characteristics of journal publication, annual trends, and differences in impact factors (IFs) of journal publications. This study is a secondary bibliometric analysis of articles in international journals published in PubMed over the past six years (2015-2020). The analysis compared English articles published by either UH- or CHF-affiliated GMPs in Japan in terms of, among other things, article type, research field, and IF. Of the 2372 articles analyzed, 1688 (71.2%) were published by physicians affiliated with UHs, 62.6% of which were original. Basic research, international collaboration, and ratio of IFs were significantly higher for such papers. In contrast, the number of CHF articles were significantly higher in the areas of clinical research and practice, with a greater proportion of case reports. There was no significant difference in IF between the disciplines within each affiliation, but the IF was the highest in experimental basic research and the lowest in medical and clinical education. In the six-year time series, the number of original papers by UHs and CHFs increased roughly twofold between 2015 and 2020, but the number of articles in the areas of medical education and healthcare quality and safety remained mostly unchanged. The number of international papers published by Japanese GMPs has increased since 2015, particularly in terms of original papers and clinical research from UHs. However, there was no significant difference in the IF between UH and CHF publications. Our findings can guide the development of indicators, research, and education strategies regarding Japanese GMPs' research performance.

This cross-sectional study aimed to clarify how cognitive biases and situational factors related to diagnostic errors among physicians. A self-reflection questionnaire survey on physicians’ most memorable diagnostic error cases was conducted at seven conferences: one each in Okayama, Hiroshima, Matsue, Izumo City, and Osaka, and two in Tokyo. Among the 147 recruited participants, 130 completed and returned the questionnaires. We recruited primary care physicians working in various specialty areas and settings (e.g., clinics and hospitals). Results indicated that the emergency department was the most common setting (47.7%), and the highest frequency of errors occurred during night-time work. An average of 3.08 cognitive biases was attributed to each error. The participants reported anchoring bias (60.0%), premature closure (58.5%), availability bias (46.2%), and hassle bias (33.1%), with the first three being most frequent. Further, multivariate logistic regression analysis for cognitive bias showed that emergency room care can easily induce cognitive bias (adjusted odds ratio 3.96, 95% CI 1.16−13.6, p-value = 0.028). Although limited to a certain extent by its sample collection, due to the sensitive nature of information regarding physicians’ diagnostic errors, this study nonetheless shows correlations with environmental factors (emergency room care situations) that induce cognitive biases which, in turn, cause diagnostic errors.

Virtual Patient Simulations (VPSs) have been cited as a novel learning strategy, but there is little evidence that VPSs yield improvements in clinical reasoning skills and medical knowledge. This study aimed to clarify the effectiveness of VPSs for improving clinical reasoning skills among medical students, and to compare improvements in knowledge or clinical reasoning skills relevant to specific clinical scenarios. We enrolled 210 fourth-year medical students in March 2017 and March 2018 to participate in a real-time pre-post experimental design conducted in a large lecture hall by using a clicker. A VPS program (®Body Interact, Portugal) was implemented for one two-hour class session using the same methodology during both years. A pre–post 20-item multiple-choice questionnaire (10 knowledge and 10 clinical reasoning items) was used to evaluate learning outcomes. A total of 169 students completed the program. Participants showed significant increases in average total post-test scores, both on knowledge items (pre-test: median = 5, mean = 4.78, 95% CI (4.55–5.01); post-test: median = 5, mean = 5.12, 95% CI (4.90–5.43); p-value = 0.003) and clinical reasoning items (pre-test: median = 5, mean = 5.3 95%, CI (4.98–5.58); post-test: median = 8, mean = 7.81, 95% CI (7.57–8.05); p-value < 0.001). Thus, VPS programs could help medical students improve their clinical decision-making skills without lecturer supervision.