With effective antiretroviral therapy, there is an emerging population of adults aged 50 years or older with human immunodeficiency virus (HIV). Frailty is an increasingly recognized clinical state of vulnerability associated with disability, hospitalization, and mortality. However, there is a paucity of large studies assessing its prevalence in people with HIV (PWH) aged 50 or older. PubMed was systematically searched for studies published between January 2000 and August 2020 reporting the prevalence of frailty in PWH aged 50 or older. The pooled prevalence of frailty and prefrailty was synthesized using a random-effects meta-analysis. Of the 425 studies identified, 26 studies were included in the analysis, with a total of 6584 PWH aged 50 or older. The included studies were published between 2012 and 2020, and all studies used the Fried frailty phenotype to define frailty. The overall pooled prevalence of frailty and prefrailty was 10.9% (95% confidence interval [CI], 8.1%–14.2%) and 47.2% (95% CI, 40.1%–54.4%), respectively. A high degree of heterogeneity was observed (I² = 93.2%). In the subgroup analysis, HIV-related variables and other demographic variables were examined, and heterogeneity disappeared only in the group of a longer duration since HIV diagnosis (I² = 0%). The pooled prevalence of frailty and prefrailty defined by the Fried frailty phenotype was assessed in PWH aged 50 or older. Findings from this study quantified the proportion of this specific population with this common geriatric syndrome. Future studies identifying effective strategies for frailty screening and intervention are required for this vulnerable population.

Background: Evaluating the accuracy and educational utility of artificial intelligence–generated medical cases, especially those produced by large language models such as ChatGPT-4 (developed by OpenAI), is crucial yet underexplored. Objective: This study aimed to assess the educational utility of ChatGPT-4–generated clinical vignettes and their applicability in educational settings. Methods: Using a convergent mixed methods design, a web-based survey was conducted from January 8 to 28, 2024, to evaluate 18 medical cases generated by ChatGPT-4 in Japanese. In the survey, 6 main question items were used to evaluate the quality of the generated clinical vignettes and their educational utility, which are information quality, information accuracy, educational usefulness, clinical match, terminology accuracy (TA), and diagnosis difficulty. Feedback was solicited from physicians specializing in general internal medicine or general medicine and experienced in medical education. Chi-square and Mann-Whitney U tests were performed to identify differences among cases, and linear regression was used to examine trends associated with physicians’ experience. Thematic analysis of qualitative feedback was performed to identify areas for improvement and confirm the educational utility of the cases. Results: Of the 73 invited participants, 71 (97%) responded. The respondents, primarily male (64/71, 90%), spanned a broad range of practice years (from 1976 to 2017) and represented diverse hospital sizes throughout Japan. The majority deemed the information quality (mean 0.77, 95% CI 0.75-0.79) and information accuracy (mean 0.68, 95% CI 0.65-0.71) to be satisfactory, with these responses being based on binary data. The average scores assigned were 3.55 (95% CI 3.49-3.60) for educational usefulness, 3.70 (95% CI 3.65-3.75) for clinical match, 3.49 (95% CI 3.44-3.55) for TA, and 2.34 (95% CI 2.28-2.40) for diagnosis difficulty, based on a 5-point Likert scale. Statistical analysis showed significant variability in content quality and relevance across the cases (P<.001 after Bonferroni correction). Participants suggested improvements in generating physical findings, using natural language, and enhancing medical TA. The thematic analysis highlighted the need for clearer documentation, clinical information consistency, content relevance, and patient-centered case presentations. Conclusions: ChatGPT-4–generated medical cases written in Japanese possess considerable potential as resources in medical education, with recognized adequacy in quality and accuracy. Nevertheless, there is a notable need for enhancements in the precision and realism of case details. This study emphasizes ChatGPT-4’s value as an adjunctive educational tool in the medical field, requiring expert oversight for optimal application.

With advances in antiretroviral therapy and subsequent increase in life expectancy, People with HIV (PWH) now experience multiple geriatric syndromes in the setting of advanced aging and increased multimorbidity. HIV clinicians bear the responsibility of delivering geriatric care to this vulnerable population, despite limited geriatric medicine training and limited support from HIV service networks that were not traditionally designed to care for an aging population. Although HIV clinicians reported formal guidelines specific to older PWH to be among the most helpful interventions, current HIV guidelines present multiple issues in their applicability to the care of older PWH, including multifactorial nature of conditions in older adults, difficulty measuring patient-centered outcomes, lack of representation of older PWH in clinical trials, limited guidelines addressing geriatric syndromes, and the use of chronological age as criteria for inclusion despite advanced aging in PWH. Understanding that updated guidelines addressing above challenges may take many years to develop, we offer strategies on the application of current guidelines, including using baseline attributes, time to benefit, and the Geriatrics 5M model to aid in shared decision making and improve outcomes among older PWH.

Background: Generative artificial intelligence (AI) is increasingly used in medical education, including AI-based virtual patients to improve interview skills. However, how much AI-based assessment (ABA) differs from human-based assessment (HBA) remains unclear. Objective: This study aimed to compare the quality of clinical interview assessments generated via an ABA (GPT-o1 Pro [ABA-o1] and GPT-5 Pro [ABA-5]) with those generated via an HBA conducted by clinical instructors in an AI-based virtual patient setting. We also examined whether AI reduced evaluation time and assessed agreement across participants with different levels of clinical experience. Methods: A standardized case of leg weakness was implemented in an AI-based virtual patient. Seven participants (2 medical students, 3 residents, and 2 attending physicians) each conducted an interview with the AI patient, and transcripts were scored using the 25-item Master Interview Rating Scale (0‐125). Three evaluation strategies were compared. First, GPT-o1 Pro and GPT-5 Pro scored each transcript 5 times with different random seeds to test case specificity. Processing time was logged automatically. Second, 5 blinded clinical instructors independently rated each transcript once using the same rubric. Third, reliability metrics were applied. For AI, intraclass correlation coefficients (ICCs) quantified repeatability. For humans, the ICC(2,1) was calculated. Agreement was quantified using the Pearson r , Lin concordance correlation coefficient, Bland-Altman limits of agreement, Cronbach α, and ICC. Time efficiency was expressed as mean minutes per transcript and relative percentage reduction. Results: Mean interview scores were similar across methods (ABA-o1: mean 52.1, SD 6.9; ABA-5: mean 53.2, SD 6.8; HBA: mean 53.7, SD 6.8). Agreement between ABA and HBA was strong ( r =0.90; concordance correlation coefficient=0.88) with minimal bias (ABA-o1: mean 0.4, SD 2.7; ABA-5: mean 1.5, SD 5.2; limits of agreement: –4.9 to 5.7 for ABA-o1 and –8.6 to 11.7 for ABA-5). The Cronbach α was 0.81 (ABA-o1), 0.86 (ABA-5), and 0.80 (HBA); the ICC(3,1) was 0.77 (ABA-o1) and 0.82 (ABA-5); and the ICC(2,1) was 0.38 (HBA). The coefficient of variation for ABA was approximately half that of HBA (6.6% vs 13.9%). Processing time for 5 runs was 4 minutes, 19 seconds for ABA-o1 and 3 minutes, 20 seconds for ABA-5 vs 10 minutes, 16 seconds for physicians, corresponding to 58% and 67.6% reductions, respectively. Conclusions: ABA-o1 and ABA-5 produced scores closely matching HBA while demonstrating superior consistency and reliability. In the setting of virtual interview transcripts, these findings suggest that ABA may serve as a valid, rapid, and scalable alternative to HBA, reducing per-assessment time by over half. Applied strategically, AI-based scoring could enable timely feedback, improve efficiency, and reduce faculty workload. Further research is needed to confirm generalizability across broader settings.

Background: Japan faces unprecedented demographic challenges as the world's most rapidly aging country, with 29.4% of the population aged ≥ 65 (2025) and projections estimating 38% by 2065. While Comprehensive Geriatric Assessment (CGA) is the gold standard for geriatric care, its implementation in Japan remains limited to geriatric specialists, creating a significant gap in delivering optimal care to Japan's growing older adult population. Objective: To propose an integrated model that combines the 5Ms framework (Mind, Mobility, Medications, Multicomplexity, Matters Most) with CGA to enhance accessibility, practicality, and continuity of geriatric care in Japanese healthcare systems. Methods: This position statement synthesizes evidence on CGA effectiveness and the educational and clinical utility of the 5Ms, evaluates cultural considerations in Japan, and proposes a three‐tiered implementation model. Results: The integration of CGA and the 5Ms framework creates a tiered assessment continuum that adapts to clinical contexts, facilitates team‐based care, and improves quality and continuity across settings. The model supports education, quality improvement, and culturally sensitive care planning. Conclusions: The integration of the 5Ms and CGA offers a practical framework to formalize, connect, and strengthen geriatric care already being delivered across Japanese healthcare settings. The Japan Geriatrics Society supports this model as a guiding structure for phased implementation, workforce development, and ongoing evaluation to enhance the delivery of person‐centered geriatric care in Japan.

Objectives: To assess the trends in palliative care consultation utilization and identify associated factors among older adults hospitalized with hip fractures in the United States between 2016 and 2020. Methods: We conducted a retrospective cohort study using data from the National Inpatient Sample from 2016 to 2020. The study included patients aged 65 and older admitted to hospital with a primary diagnosis of hip fracture. We identified palliative care consultations using ICD-10 code Z51.5. Multivariate logistic regression analyses were performed to identify predictors of palliative care utilization, adjusting for demographics, clinical variables, and hospital characteristics. Results: A total of 293,749 admissions for hip fractures were identified, of which 9546 (3.2%) had palliative care consultations. A consistent upward trend was seen in the proportion of patients receiving palliative care consultations across all fracture types. Patients of color (Black: odds ratio [OR] = 0.73, 95% confidence interval [CI]: 0.65-0.83; Hispanic: OR = 0.67, 95% CI: 0.60-0.75 compared to White), those in lower-income quartiles (lowest: OR = 0.81, 95% CI: 0.76-0.87 compared to highest), smaller hospital size (OR = 0.84, 95% CI: 0.79-0.89 compared to large), and rural hospital (OR = 0.63, 95% Cl: 0.59-0.68, compared to urban teaching) were associated with fewer palliative care consultations. Conclusion: The number of palliative care consultations during hip fracture hospital admission was low, although the proportion increased over time. Significant disparities were observed and further research should explore barriers to palliative care access and develop strategies to enhance its delivery across diverse healthcare settings.