Journal of the American Medical Informatics Association最新文献

{"title":"Augmenting clinical decision-making with large language models: evaluation across general and specialty tasks.","authors":"Yuechen Tao, Xiushi Lin, He Sun, Wenrui Bao, Xiaobo Zhu, Shiwei Wu, Yixiao Liu, Ziang Li, Jialong Wu, Zhenkun Zhao, Lei Qiu, Qianyang Ni, Song Zhang, Zhenchao Tang, Zuobin Ying, Yu An, Jie Tian, Zhenyu Liu, Jian Lu","doi":"10.1093/jamia/ocag053","DOIUrl":"10.1093/jamia/ocag053","url":null,"abstract":"<p><strong>Objective: </strong>Large language models (LLMs) have shown promise in clinical applications, yet prior studies mainly evaluated their standalone performance on benchmarks or examinations. This study assesses how LLMs support clinicians across specialties, disease contexts, experience levels and decision-making stages.</p><p><strong>Methods: </strong>We evaluated 3 LLMs (Deepseek-R1, GPT-4o-mini and LLaMA-4) using 2 task types: (1) general-disease tasks spanning specialties and disease incidence levels, and (2) prostate disease scenario covering the clinical workflow, including diagnosis, treatment planning, postoperative rehabilitation and prognosis. Clinicians with different seniority completed tasks independently and repeated them after reviewing LLM-generated responses. Performance was rated by experts using a 5-point Likert scale, and differences were analyzed with the Wilcoxon signed-rank test.</p><p><strong>Results: </strong>LLM assistance significantly improved clinician performance across general disease tasks (P < .05). In the specialty scenario, junior clinicians' scores increased by 0.579-0.723 (15.9%-20.8%) across 4 clinical stages, whereas senior clinicians' improvements ranged from 0.053-0.303 (1.3%-7.5%). With LLM support, junior clinicians surpassed senior clinicians' unaided performance in postoperative rehabilitation and prognosis stages (P < .05) and achieved comparable performance in diagnosis and treatment stages (P > .05). Performance varied among models, with Deepseek-R1 performing best in diagnostic tasks.</p><p><strong>Discussion: </strong>These findings suggest that LLM assistance may provide greater benefit to less-experienced clinicians and are particularly effective in downstream decision-making stages, indicating a potential role in mitigating experience-related performance disparities.</p><p><strong>Conclusions: </strong>LLMs may serve as supportive clinical-decision support tools across diseases, specialties, clinician experience levels, and workflow stages, with observed improvements in decision quality and reduced performance disparities.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1660-1670"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Are asynchronous or synchronous clinical decision support more likely to change provider behavior? A case study in dementia.","authors":"","doi":"10.1093/jamia/ocag120","DOIUrl":"10.1093/jamia/ocag120","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1847"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reasoning or reciting? A temporal contamination audit of large language models in clinical medicine.","authors":"Alexander P Sheppert, Brian Adams, Andrew D Sheppert, Sylvia Riley","doi":"10.1093/jamia/ocag069","DOIUrl":"10.1093/jamia/ocag069","url":null,"abstract":"<p><strong>Objective: </strong>Evaluate whether large language models reason or simply regurgitate training data in clinical diagnosis.</p><p><strong>Materials and methods: </strong>We audited 2000 clinical case reports from PubMed Central: 1000 from 2021 to 2022 (within training data) and 1000 from 2025 (after training cutoffs). Five frontier LLMs generated diagnoses evaluated by an independent AI judge validated against physician consensus (n = 10 000 evaluations).</p><p><strong>Results: </strong>Diagnostic accuracy was virtually identical across temporal cohorts (66.8% contaminated vs 66.9% clean), directly contradicting the memorization hypothesis. Lexical similarity was uniformly low (mean ROUGE-L 0.057), and semantic similarity measured by BERTScore showed no memorization signal (F1 0.8182 contaminated vs 0.8195 clean, Δ = +0.0013), confirming that models generate novel reasoning rather than regurgitating training data.</p><p><strong>Discussion: </strong>This large-scale audit, using both lexical and semantic similarity metrics, provides compelling evidence that LLMs engage in genuine clinical reasoning rather than regurgitating memorized training data.</p><p><strong>Conclusion: </strong>Models demonstrated equivalent accuracy on cases they could not have seen during training, suggesting they have internalized generalizable medical knowledge rather than memorizing specific cases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1617-1626"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reporting follow-up budgets for diagnostic and predictive AI.","authors":"Henry Bair, Mak Djulbegovic","doi":"10.1093/jamia/ocag118","DOIUrl":"10.1093/jamia/ocag118","url":null,"abstract":"<p><strong>Objective: </strong>To argue that diagnostic and predictive AI should be evaluated by both classification performance and the downstream work their outputs create.</p><p><strong>Discussion: </strong>Prior work has framed AI as an intervention and has quantified alert burden or decision utility under resource constraints. This perspective extends that logic beyond surveillance alerts by proposing follow-up budget reporting for diagnostic and predictive AI. Across lung nodule detection, sepsis alerting, and autonomous diabetic retinopathy screening, similar classification performance can imply very different demands on imaging, specialist access, nursing attention, and patient waiting. A follow-up budget statement should specify the comparator, scope and stopping rule, downstream action volume, resource types consumed, yield per clinically meaningful true-positive case, and the local pathway assumptions and stakeholders responsible for those estimates.</p><p><strong>Conclusion: </strong>Clinical AI evaluation should make follow-up capacity visible alongside discrimination, calibration, and decision-analytic measures.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1839-1842"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148406712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A national survey of clinical informatics educational opportunities in United States medical schools.","authors":"Gha Young Lee, Kathryn Li, Advait Patil, Sharon Jiang, Christy Zheng, Jessika Baral, Jacqueline You, Ellen Kim","doi":"10.1093/jamia/ocag085","DOIUrl":"10.1093/jamia/ocag085","url":null,"abstract":"<p><strong>Objective: </strong>To assess prevalence, characteristics, and institutional predictors of clinical informatics (CI) education and student organizations in US allopathic (MD) and osteopathic (DO) medical schools.</p><p><strong>Materials and methods: </strong>We reviewed 222 US medical schools from the 2024 Medical School Admission Requirements (MSAR) and American Association of Colleges of Osteopathic Medicine (AACOM) databases. Using predefined criteria, we identified CI-related courses and student groups and abstracted institutional characteristics including affiliated CI fellowships. Bivariate and multivariable logistic regression identified predictors.</p><p><strong>Results: </strong>Of 222 schools, 30.2% offered at least one CI course and 23.0% had a student group. In bivariate analyses, MD programs and institutions with CI fellowships were significantly more likely to offer courses (both P < 0.001). In multivariable analyses, MD program type was the strongest predictor (adjusted odds ratio [aOR]=6.58, 95% confidence interval [CI] 2.22-22.41), followed by CI fellowship presence (aOR = 3.36), private school status (aOR = 2.08), and class size (aOR = 1.01). All 51 student groups were at MD institutions, and urban setting was associated with group presence (P = 0.034).</p><p><strong>Discussion: </strong>The association with CI fellowships suggests a relationship between graduate and undergraduate medical education institutions. The association with MD programs could be influenced by differing curricular demands and flexibility. The association with urban settings may reflect the role of local innovation ecosystems.</p><p><strong>Conclusion: </strong>CI educational opportunities vary across US medical schools, concentrated at MD programs and institutions with GME-level infrastructure. These findings establish a baseline for CI opportunities and highlight the need to understand whether institutional differences translate to measurable competency gaps.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1671-1678"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13389958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From exchange to action: how hospitals receive and use health-related social needs data from external sources.","authors":"Chelsea Richwine, Wei Chang, Bradley Iott","doi":"10.1093/jamia/ocag092","DOIUrl":"10.1093/jamia/ocag092","url":null,"abstract":"<p><strong>Objective: </strong>To understand how hospitals receive and use data on patients' health-related social needs (HRSN) from external sources, and how methods of receipt relate to use.</p><p><strong>Materials and methods: </strong>Using 2024 nationally representative survey data on hospitals (N = 2146) we described external sources of receiving HRSN data and variation by hospital characteristics, and how methods of receipt relate to uses of data to support patient care.</p><p><strong>Results: </strong>In 2024, 70% of hospitals received HRSN data from external sources-via health information exchanges (HIEs) (51%), electronic health record (EHR) vendor networks (40%), national networks (36%), referral platforms (28%), other healthcare organizations (24%) and community-based organizations (20%). Large, urban, system-affiliated, non-profit and government-owned hospitals, value-based care participants, and those with an Epic EHR had higher rates of receiving HRSN data from any source compared to their counterparts. About half of hospitals reported using data received for various purposes (e.g., referrals, discharge planning). Hospitals that received data via HIEs or EHR vendor networks or directly from other organizations were more likely to use data across measured uses. Hospitals that received data via referral platform were more likely to use data for referrals, informing community needs, and population health analytics.</p><p><strong>Discussion: </strong>Many hospitals receive HRSN data from external sources, often through existing connections to health information networks, which was associated with greater internal and downstream use.</p><p><strong>Conclusion: </strong>Efforts to improve bi-directional exchange between health and social service organizations may enable hospitals to more effectively identify and address patients' HRSN.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1627-1635"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospectively evaluating the environmental impacts of digital health applications: a case study and recommendations.","authors":"Clément Morand, Aurélie Névéol, Rosy Tsopra, Anne-Isabelle Tropeano, Sophie de Chambine, Cécile Klinguer, Anne-Laure Ligozat","doi":"10.1093/jamia/ocag091","DOIUrl":"10.1093/jamia/ocag091","url":null,"abstract":"<p><strong>Objectives: </strong>To leverage the evaluation of the environmental impact of the Direct AP-HP/Lorah e-referral service offered in Paris (France) hospitals to create recommendations for evaluating the impact of digital health services.</p><p><strong>Materials and methods: </strong>We review the tools and methods currently available to measure the carbon footprint and electricity consumption of digital services in the context of Life Cycle Assessment (LCA) for a comprehensive evaluation. We use the recent deployment of a telemedicine communication service at a major French hospital as a case study to understand the practical implications of conducting an impact study. Three-deployment scenarii are considered: current usage, double usage and maximum capacity.</p><p><strong>Results: </strong>The bulk of the carbon footprint of the Direct AP-HP/Lorah service is due to servers vs network and user terminals in all scenarios considered. Computing hardware production impacts was instrumental in the overall impact assessment, as embodied impact represent 45% of Carbon footprint and the most of Metallic resource depletion. Recommendations for further studies notably include adequate anticipation of service usage and data collection.</p><p><strong>Discussion: </strong>The environmental impact of the new telemedicine service could be assessed in sufficient level of details to provide decision makers with an adequate comparison of the service with alternative email communication.</p><p><strong>Conclusion: </strong>The recommendations derived from this use case should facilitate adequate impact data collection for future studies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1726-1734"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data.","authors":"Ritoban Kundu, Maxwell Salvatore, Kumar Kshitij Patel, Lucila Ohno-Machado, Hyunghoon Cho, Xu Shi, Bhramar Mukherjee","doi":"10.1093/jamia/ocag083","DOIUrl":"10.1093/jamia/ocag083","url":null,"abstract":"<p><strong>Objectives: </strong>To develop privacy-enhancing statistical methods for estimating disease risk parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, avoiding individual-level data sharing. We illustrate their utility via a cross-biobank analysis of smoking and 97 cancer subtypes using NIH All of Us (AOU) and Michigan Genomics Initiative (MGI) data sites.</p><p><strong>Materials and methods: </strong>Distributed health platforms often render centralized algorithms infeasible due to patient privacy protection. We propose Sequential Pseudo-Likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW) to adjust for selection bias using summary statistics shared across sites and external population information. SAIPW employs flexible auxiliary models for multiple robustness. We compared SPL and SAIPW against unweighted and centralized/meta-learning benchmarks in simulations, applying them to harmonized MGI (n = 50 935) and AOU (n = 241 563) data.</p><p><strong>Results: </strong>Unweighted estimators exhibited substantial bias. SPL and SAIPW yielded unbiased estimates with valid coverage, with SAIPW remaining robust to selection model misspecification. Both approaches showed negligible efficiency loss relative to centralized methods. Meta-learning methods proved unstable for rare outcomes. Real-data analyses consistently identified strong associations between smoking and lung, bladder, and larynx cancers.</p><p><strong>Discussion: </strong>These findings highlight the necessity of adjusting for site-specific selection biases in distributed health networks. SPL and SAIPW offer practical, scalable solutions that bypass the instability of meta-analysis for rare events, successfully harmonizing diverse biobanks while strictly enhancing patient privacy.</p><p><strong>Conclusion: </strong>Our framework enables valid, privacy-enhancing inference across EHR sites subject to heterogeneous selection, facilitating scalable, distributed research using real-world data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1679-1692"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing emergency clinician engagement with social drivers of health data among patients with opioid use disorder.","authors":"Melanie F Molina, Samuel D Pimentel, Cynthia Fenton, Julia Adler-Milstein, Laura M Gottlieb","doi":"10.1093/jamia/ocag093","DOIUrl":"10.1093/jamia/ocag093","url":null,"abstract":"<p><strong>Objectives: </strong>To characterize emergency department (ED) clinician engagement with electronic health record (EHR)-based social drivers of health (SDOH) data; test whether engagement differs in encounters with opioid use disorder (OUD); and, among OUD encounters, assess whether engagement is associated with medications for OUD (MOUD).</p><p><strong>Materials and methods: </strong>We conducted a cross-sectional study of adult ED encounters (January 2023-October 2024). OUD encounters, identified with a structured phenotype, were matched (1:2) to non-OUD encounters. Audit logs captured clinician engagement with structured SDOH questions (\"SDOH Wheel\"), ICD-10 Z codes in the Problem List, Social History free text, and social work notes. Engagement was any SDOH documentation or review of preexisting SDOH data during the encounter. Logistic regression estimated associations.</p><p><strong>Results: </strong>Among 17 103 encounters (5701 OUD; 11 402 non-OUD), clinician SDOH documentation was rare (<1%). Clinicians most often reviewed the SDOH Wheel (1103/3953; 27.9%), followed by social work notes (1711/10 670; 16.0%), Z codes (29/620; 4.7%), and Social History free text (232/6942; 3.3%). Engagement occurred in 17.1% of encounters and was higher with OUD (23.1% vs 14.1%; OR 1.84, 95% CI 1.70-2.00). Among OUD encounters, engagement was not associated with MOUD (OR 1.27, 95% CI 0.95-1.70); however, MOUD treatment varied by race and ethnicity, reflecting persistent disparities.</p><p><strong>Discussion: </strong>ED clinicians infrequently documented and favored review of structured, accessible SDOH data. Engagement increased in OUD encounters yet neither predicted MOUD nor mitigated racial and ethnic treatment disparities. EHR interfaces that surface SDOH data, coupled with targeted decision supports, might influence equitable, time-sensitive ED care.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1693-1701"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Veteran health information exchange volume and 30-day readmissions, avoidable hospitalizations, and in-hospital mortality: evidence from community and Veterans Health Administration direct care.","authors":"Abbie Zhang, Daniel Lipsey, Stuart Figueroa, Megan E Price, Jonathan Nebeker, Eric Chung Ren Pan, Kevin Griffith, Steven D Pizer","doi":"10.1093/jamia/ocag125","DOIUrl":"10.1093/jamia/ocag125","url":null,"abstract":"<p><strong>Objective: </strong>To determine whether a greater volume of health information exchange (HIE) between care delivery systems improves patient outcomes and whether effects differ between community care and Veterans Health Administration (VHA) direct care settings.</p><p><strong>Materials and methods: </strong>We used electronic health record data for all VHA enrollees from January 2022 to December 2023 (3144 medical center-months; 2.4 million patients per month). HIE volume, the number of unique patient-days with completed exchanges, was our key explanatory variable. Primary outcomes were 30-day readmissions, avoidable hospitalizations, and in-hospital mortality. We instrumented for HIE volume using each medical center's count of organizational exchange partners and estimated models overall and by care setting with medical center and month fixed effects and direct patient volume controls.</p><p><strong>Results: </strong>In community care, a 1-SD increase in HIE volume was associated with 4.07 fewer 30-day readmissions (95% CI, -4.62 to -3.51), 1.45 fewer avoidable hospitalizations (95% CI, -2.84 to -0.06), and 0.25 fewer inpatient deaths (95% CI, -0.44 to -0.06) per medical center per month. In VHA direct care, the same increase in HIE volume was associated with 7.07 additional readmissions (95% CI, 6.46-7.67) and 0.19 additional inpatient deaths (95% CI, 0.03-0.35), with no significant impact on avoidable hospitalizations (95% CI, -0.96 to 1.68).</p><p><strong>Conclusion: </strong>Setting-specific effects may stem from asymmetric content (longitudinal histories outbound from VHA, episodic summaries inbound to VHA), asymmetric interfaces (modern community EHRs vs VHA's Joint Legacy Viewer), and HIE-enabled reclamation of community patients into VHA. More work is needed to disentangle these mechanisms.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1770-1779"},"PeriodicalIF":7.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148609540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}