Nevin Manimala

Hepatol Commun. 2025 Nov 20;9(12):e0857. doi: 10.1097/HC9.0000000000000857. eCollection 2025 Dec 1.

ABSTRACT

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common pediatric liver disease, yet known biological risk factors do not fully explain its development. This study evaluated the association between social determinants of health (SDH)-including income, education, housing, and environmental factors-and MASLD in children with overweight or obesity.

METHODS: This cross-sectional study included children with overweight or obesity evaluated in pediatric gastroenterology clinics, stratified by the presence of MASLD. Participants’ residential census tracts were linked to the California Healthy Places Index (HPI), where higher scores indicate greater socioeconomic advantage. Generalized linear regression models, adjusted for age, sex, race, ethnicity, and body mass index (BMI), assessed associations between SDH and MASLD.

RESULTS: The study included 888 children (mean age: 12.8±2.9 y, mean BMI: 30.6±6.4 kg/m2). Children with MASLD lived in neighborhoods with significantly lower HPI scores than those without MASLD (-0.22 vs. -0.03; 41st vs. 49th percentile, p=0.01). They also resided in areas with significantly lower socioeconomic advantage across multiple HPI subdomains, including housing (-0.16 vs. 0.03; 44th vs. 51st percentile, p<0.001) and economic (-0.38 vs. -0.12; 36th vs. 55th percentile, p<0.001) scores.

CONCLUSIONS: Children with MASLD were more likely to live in neighborhoods with greater socioeconomic and environmental disadvantage than their peers with overweight or obesity but without MASLD. These findings highlight potential SDH targets for public health interventions aimed at reducing MASLD risk in children.

PMID:41264907 | DOI:10.1097/HC9.0000000000000857

J Craniofac Surg. 2025 Nov 20. doi: 10.1097/SCS.0000000000012190. Online ahead of print.

ABSTRACT

This study aims to compare the clinical outcomes of bioresorbable and titanium fixation systems used in anterior maxillary segmental osteotomy (AMSO) procedures, with a focus on skeletal stability, postoperative complications, and hardware-related morbidity. A prospective, randomized clinical trial was conducted on 50 patients undergoing AMSO. Participants were randomly assigned to 2 groups: bioresorbable fixation using poly-L-lactic acid/polyglycolic acid (PLLA/PGA) (n=15) and conventional titanium miniplates (n=35). Standardized panoramic and cephalometric radiographs were taken preoperatively and at 1 and 12 months postoperatively. Primary outcome measures included skeletal stability assessed by horizontal and vertical movement of the anterior maxillary segment. Secondary outcomes included postoperative infection, wound dehiscence, plate palpability, radiographic verification of correct segmental positioning, and the need for hardware removal. At 1 and 12 months, cephalometric radiographs in both groups demonstrated comparable skeletal stability, with no statistically significant differences in relapse rates. The incidence of infection and wound dehiscence was similar across groups. However, the titanium group had a significantly higher rate of hardware removal requests (8.6% vs. 0%, P < 0.05). No major adverse events were reported in either group. Bioresorbable fixation systems provide comparable skeletal stability to titanium plates in AMSO, while eliminating the need for secondary hardware removal. These findings support the selective use of bioresorbable fixation, particularly in patients for whom hardware removal is undesirable.

PMID:41264905 | DOI:10.1097/SCS.0000000000012190

JCO Precis Oncol. 2025 Nov;9:e2500560. doi: 10.1200/PO-25-00560. Epub 2025 Nov 20.

ABSTRACT

In clinical trials, the initial step typically involves assessing a new drug’s toxicity profile, aiming to identify a tolerable dose level for subsequent studies. In phase I trials, the primary objective is to determine the maximum tolerated dose, defined as the highest dose associated with an acceptable level of toxicity. Numerous methods have been developed to guide dose escalation and de-escalation decisions during trial conduct. Among these approaches, the calibration-free odds (CFO) design has demonstrated superior operating characteristics and has emerged as one of the most effective approaches for dose finding. To facilitate the application of the CFO design in clinical trial practice, an R package and a Shiny app have been released. This study presents CFO decision tables in Excel files to further remove the barrier of applying the CFO design to real trials. Anyone involved in the trial conduct can implement the CFO design with no difficulties. During the trial, dose movement decisions can be made simply by referring to the cumulative data (including numbers of patients treated and observed toxicities) and the pregenerated decision tables, without any additional statistical calculation. This approach significantly enhances the usability of the CFO design and reduces the operational complexity associated with its implementation in clinical trials. The Excel CFO decision tables can be downloaded from CFO Shiny App.

PMID:41264898 | DOI:10.1200/PO-25-00560

JMIR Med Inform. 2025 Nov 20;13:e66764. doi: 10.2196/66764.

ABSTRACT

BACKGROUND: In Switzerland, sexual assault reports have historically been documented on paper, which limited standardization, completeness, and challenges to produce reliable statistics.

OBJECTIVE: This study describes the development and implementation of an Electronic Sexual Assault Record (eSAR) within Geneva University Hospitals’ Electronic Medical Record (EMR) system, with the aim of improving data quality, documentation, and multidisciplinary coordination.

METHODS: The eSAR was developed by a multidisciplinary team including forensic doctors, gynecologists, nurses (clinical and informatics), epidemiologists, and IT specialists. Its structure was based on existing hospital protocols and international recommendations. Variables were defined as “essential” or “highly recommended,” with structured fields to ensure completeness and comparability. Confidentiality was safeguarded through restricted access and regular audits.

RESULTS: The eSAR was launched in June 2022 and revised in 2023 after user feedback and training. Since implementation, 382 reports have been completed. Data quality improved substantially, with major reductions in missing information. The system also streamlined workflows and strengthened collaboration across specialties.

CONCLUSIONS: The eSAR improved documentation and data reliability, providing a replicable model for standardized sexual assault reporting in Switzerland.

PMID:41264873 | DOI:10.2196/66764

JMIR Form Res. 2025 Nov 20;9:e76618. doi: 10.2196/76618.

ABSTRACT

BACKGROUND: The integration of artificial intelligence (AI) in medical education is evolving, offering new tools to enhance teaching and assessment. Among these, script concordance tests (SCTs) are well-suited to evaluate clinical reasoning in contexts of uncertainty. Traditionally, SCTs require expert panels for scoring and feedback, which can be resource-intensive. Recent advances in generative AI, particularly large language models (LLMs), suggest the possibility of replacing human experts with simulated ones, though this potential remains underexplored.

OBJECTIVE: This study aimed to evaluate whether LLMs can effectively simulate expert judgment in SCTs by using generative AI to author, score, and provide feedback for SCTs in cardiology and pneumology. A secondary objective was to assess students’ perceptions of the test’s difficulty and the pedagogical value of AI-generated feedback.

METHODS: A cross-sectional, mixed methods study was conducted with 25 second-year medical students who completed a 32-item SCT authored by ChatGPT-4o (OpenAI). Six LLMs (3 trained on the course material and 3 untrained) served as simulated experts to generate scoring keys and feedback. Students answered SCT questions, rated perceived difficulty, and selected the most helpful feedback explanation for each item. Quantitative analysis included scoring, difficulty ratings, and correlations between student and AI responses. Qualitative comments were thematically analyzed.

RESULTS: The average student score was 22.8 out of 32 (SD 1.6), with scores ranging from 19.75 to 26.75. Trained AI systems showed significantly higher concordance with student responses (ρ=0.64) than untrained models (ρ=0.41). AI-generated feedback was rated as most helpful in 62.5% of cases, especially when provided by trained models. The SCT demonstrated good internal consistency (Cronbach α=0.76), and students reported moderate perceived difficulty (mean 3.7, SD 1.1). Qualitative feedback highlighted appreciation for SCTs as reflective tools, while recommending clearer guidance on Likert-scale use and more contextual detail in vignettes.

CONCLUSIONS: This is among the first studies to demonstrate that trained generative AI models can reliably simulate expert clinical reasoning within a script-concordance framework. The findings suggest that AI can both streamline SCT design and offer educationally valuable feedback without compromising authenticity. Future studies should explore longitudinal effects on learning and assess how hybrid models (human and AI) can optimize reasoning instruction in medical education.

PMID:41264864 | DOI:10.2196/76618

Prim Care Companion CNS Disord. 2025 Nov 20;27(6):25m03995. doi: 10.4088/PCC.25m03995.

ABSTRACT

Objective: To explore the effect of a patient’s suicide on mental health professionals (MHPs), the perceived psychological and professional impacts, the support MHPs require versus actually receive, and their views on training that is provided to cope with such incidents.

Methods: A mixed-methods approach was used. An online survey was conducted from September to October 2023. The validated semistructured questionnaire was open for 8 weeks and covered demographics, details of incidents, emotional and professional impacts, and support systems. Responses were analyzed using descriptive statistics and thematic analysis to derive insights from qualitative data.

Results: Among 96 responses, 51% had treated patients who died by suicide. These patients were mostly males, primarily diagnosed with psychotic or affective disorders. Of the MHP respondents, 76.6% experienced suicide of a patient after completing their training. Around one-third reported moderate-to-extreme emotional impact of the incident, with sadness, regret, and guilt being common responses. Support-seeking behaviors were common with 52.2% of respondents finding support from colleagues, family, or professional communities helpful, but formal training on managing patient suicide was found to be lacking.

Conclusion: Patient suicide can impact MHPs, affecting emotional well-being, professional identity, and personal life, emphasizing the importance of establishing a supportive environment, incorporating enhanced training into psychiatry programs, and encouraging open dialog.

Prim Care Companion CNS Disord 2025;27(6):25m03995.

Author affiliations are listed at the end of this article.

PMID:41264862 | DOI:10.4088/PCC.25m03995

JMIR Med Educ. 2025 Nov 20;11:e80084. doi: 10.2196/80084.

ABSTRACT

BACKGROUND: Video-sharing sites such as YouTube (Google) and TikTok (ByteDance) have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (AI) tools, however, has resulted in low-quality, AI-generated material (commonly called “slop”) cluttering these platforms and competing with authoritative educational materials. The extent to which slop has polluted science education video content is unknown, as are the specific hazards to learning from purportedly educational videos made by AI without the use of human discretion.

OBJECTIVE: This study aimed to advance a formal definition of slop (based on the recent theoretical construct of “careless speech”), to identify its qualitative characteristics that may be problematic for learners, and to gauge its prevalence among preclinical biomedical science (medical biochemistry and cell biology) videos on YouTube and TikTok. We also examined whether any quantitative features of video metadata correlate with the presence of slop.

METHODS: An automated search of publicly available YouTube and TikTok videos related to 10 search terms was conducted in February and March 2025. After exclusion of duplicates, off-topic, and non-English results, videos were screened, and those suggestive of AI were flagged. The flagged videos were subject to a 2-stage qualitative content analysis to identify and code problematic features before an assignment of “slop” was made. Quantitative viewership data on all videos in the study were scraped using automated tools and compared between slop videos and the overall population.

RESULTS: We define “slop” according to the degree of human care in production. Of 1082 videos screened (814 YouTube, 268 TikTok), 57 (5.3%) were deemed probably AI-generated and low-quality. From qualitative analysis of these and 6 additional AI-generated videos, we identified 16 codes for problematic aspects of the videos as related to their format or contents. These codes were then mapped to the 7 characteristics of careless speech identified earlier. Analysis of view, like, and comment rates revealed no significant difference between slop videos and the overall population.

CONCLUSIONS: We find slop to be not especially prevalent on YouTube and TikTok at this time. These videos have comparable viewership statistics to the overall population, although the small dataset suggests this finding should be interpreted with caution. From the slop videos that were identified, several features inconsistent with best practices in multimedia instruction were defined. Our findings should inform learners seeking to avoid low-quality material on video-sharing sites and suggest pitfalls for instructors to avoid when making high-quality educational materials with generative AI.

PMID:41264860 | DOI:10.2196/80084

JMIR Form Res. 2025 Nov 20;9:e71667. doi: 10.2196/71667.

ABSTRACT

BACKGROUND: There is a need to increase health care professional training capacity to meet global needs by 2030. Effective communication is essential for delivering safe and effective patient care. Artificial intelligence (AI) technologies may provide a solution. However, evidence for high-fidelity virtual patient simulators using unrestricted 2-way verbal conversation for communication skills training is lacking.

OBJECTIVE: This study aims to compare a fully automated AI-driven voice recognition-based virtual patient simulator with traditional actor-based consultation skills simulated training in undergraduate medical students for differences in developing self-rated communication skills, student satisfaction scores, and direct cost comparison.

METHODS: Using an open-label randomized crossover design, a single web-based AI-driven communication skills training session (AI-CST) was compared with a single face-to-face actor-based consultation skills training session (AB-CST) in undergraduates at 2 UK medical schools. Offline total cohort recruitment was used, with an opt-out option. Pre-post intervention surveys using 10-point linear scales were used to derive outcomes. The primary outcome was the difference in self-reported attainment of communication skills between interventions. Secondary outcomes were differences in student satisfaction and the cost comparison of delivering both interventions.

RESULTS: Of 396 students, 378 (95%) completed at least 1 survey. Both modalities significantly increased self-reported communication skills attainment (AI-CST: mean difference 1.14, 95% CI 0.97-1.32 points; AB-CST: mean difference 1.50, 95% CI 1.35-1.66 points; both P<.001). Attainment increase was lower for AI-CST than AB-CST (by mean difference 0.36, 95% CI -0.60 to -0.13 points; P=.04). Overall satisfaction was lower for AI-CST than AB-CST (8.09 vs 9.21; mean difference -1.13, 95% CI -1.33 to -0.92 for AI-CST vs AB-CST; P<.001). The estimated costs of AI-CST and AB-CST were £33.48 (US $42.22) and £61.75 (US $77.87) per student, respectively.

CONCLUSIONS: AI-CST and AB-CST were both effective at improving self-reported communication skills attainment, but AI-CST was slightly inferior to AB-CST. Student satisfaction was significantly greater for AB-CST. Costs of AI-CST were substantially lower than AB-CST. AI-CST may provide a cost-effective opportunity to build training capacity for health care professionals.

PMID:41264856 | DOI:10.2196/71667

Diabetes Care. 2025 Dec 1;48(12):2154-2159. doi: 10.2337/dc25-1753.

ABSTRACT

OBJECTIVE: To assess the effect of automated insulin delivery (AID) on glycemic and insulin outcomes in adults with insulin-treated type 2 diabetes using a glucagon-like peptide-1 receptor agonist (GLP-1 RA).

RESEARCH DESIGN AND METHODS: In a randomized trial comparing Control-IQ+ AID versus continuation of prestudy insulin delivery method plus continuous glucose monitoring (CGM group), 143 (45%) of the 319 participants were using a GLP-1 RA at baseline, which was continued during the trial.

RESULTS: Among GLP-1 RA users, mean HbA1c decreased by 0.8% from a baseline of 8.0 ± 1.2% with AID, which represented a mean improvement of -0.5% (95% CI -0.8 to -0.3, P < 0.001) compared with the CGM group. Time-in-range 70-180 mg/dL and other CGM metrics reflective of hyperglycemia also showed comparable statistically significant improvements using AID when added to GLP-1 RA use. For GLP-1 RA users, there was no significant difference in weight after 13 weeks with AID compared with the CGM group (0.9 kg, 95% CI -0.2 to 2.1, P = 0.10), whereas, in GLP-1 RA nonusers, there was a mean weight gain of 1.9 kg with AID compared with CGM (95% CI 0.5 to 3.2, P = 0.007).

CONCLUSIONS: The benefits of AID appear to be substantial for a broad spectrum of insulin-treated patients with type 2 diabetes, including those already receiving contemporary and guideline-directed therapy, such as a GLP-1 RA medication. These additive benefits of AID in GLP-1 RA users included significant reductions in HbA1c levels with simultaneous reduction in insulin use, along with no statistical increase in weight despite very significant improvements in glycemic control.

PMID:41264828 | DOI:10.2337/dc25-1753

N Z Med J. 2025 Nov 21;138(1626):49-61. doi: 10.26635/6965.6989.

ABSTRACT

AIM: We investigated Māori and Pacific adults with type 2 diabetes (T2D) to determine the prevalence of latent autoimmune diabetes in adults (LADA), assess the type 1 diabetes (T1D) genetic risk score (GRS) distribution in those with and without autoantibodies and investigate differences in clinical diabetes characteristics based on autoantibody presence or a high T1D GRS.

METHOD: A total of 2,538 Māori and Pacific participants from the Genetics of Gout, Diabetes, and Kidney Disease study in Aotearoa New Zealand were included (830 with T2D, 1,708 without). LADA was defined as age of diabetes onset >30 years, presence of autoantibodies and no insulin treatment within the first 6 months. Clinical characteristics were extracted from medical records. T1D-associated autoantibodies (glutamic acid decarboxylase, islet antigen 2, zinc transporter 8) were measured from stored blood samples from 293 participants (262 T2D, 31 without). A T1D GRS consisting of 30 single-nucleotide polymorphisms was calculated for all participants.

RESULTS: Autoantibodies were detected in 8.8% (23/262) of individuals with T2D, with 5.3% (14/262) meeting the criteria for LADA. No significant difference in T1D GRS or clinical characteristics was observed between T2D cases with and without autoantibodies. Autoantibodies were also detected in 3.2% (1/31) of participants without diabetes.

CONCLUSION: LADA is present in a subset of Māori and Pacific individuals with T2D. Autoantibody presence was not associated with differences in T1D GRS or clinical features. Further research is needed to assess whether C-peptide monitoring could guide treatment decisions in those with LADA.

PMID:41264820 | DOI:10.26635/6965.6989