BMJ Health & Care Informatics最新文献

{"title":"Explainable machine learning revealing the impact of mental and physical health on arthritis.","authors":"Md Atik Shams, Sumaiya Fatema, D M Hasibul Islam, Anindita Datta, David Eisenberg, Danastan Tasaouf Mridula, Junnatul Mawa, Asma Sultana, Nafiya Ahmed, Monowarul Islam, Tanmoy Sarkar Pias","doi":"10.1136/bmjhci-2025-101955","DOIUrl":"10.1136/bmjhci-2025-101955","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a robust and interpretable machine learning framework for arthritis risk prediction and to identify important risk factors associated with the disease.</p><p><strong>Methods: </strong>This study used four datasets, which are Behavioral Risk Factor Surveillance System (BRFSS) 2019, BRFSS 2021, National Health Interview Survey (NHIS) 2020 and NHIS 2022. We evaluated 11 machine learning and deep learning architectures including a custom stacked ensemble combined with five resampling techniques to address class imbalance and 9 imputation methods to handle missing data. Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and balanced accuracy are used to evaluate model performance, with fivefold cross-validation to ensure robustness. SHapley Additive exPlanations (SHAP) provided feature interpretability in prediction.</p><p><strong>Results: </strong>The combination of the stacked ensemble, Generative Adversarial Network (GAN) imputation and random undersampling (RUS) achieved the best performance with an AUROC of 0.8007 and sensitivity of 0.8060 in the test set of BRFSS 2019. This combination also showed a balanced performance across both subgroups of male and female by maintaining high performance with AUROC of 0.808 and 0.800, respectively, in the test set of BRFSS 2021. The stacked ensemble model with GAN imputation and either Random Undersampling (RUS) or Random Oversampling (ROS) achieved the best performance across all four datasets. SHAP analysis identified higher age, walking difficulty and high body mass index (BMI) as the top physical factors. Among the psychosocial factors, parental separation showed a high impact on predicting arthritis.</p><p><strong>Discussion: </strong>The risk of arthritis is influenced by health, lifestyle and childhood experiences. SHAP shows that difficulty walking, age, BMI and childhood stress play important roles in predicting arthritis risk.</p><p><strong>Conclusion: </strong>These results confirm important predictors of arthritis risk and provide a clear, interpretable framework.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13347896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.","authors":"Ekapob Sangariyavanich, Wanchana Ponthongmak, Nawanan Theera-Ampornpunt, Nat Tangchitnob, Gareth J McKay, Ammarin Thakkinstian","doi":"10.1136/bmjhci-2025-101997","DOIUrl":"10.1136/bmjhci-2025-101997","url":null,"abstract":"<p><strong>Objective: </strong>This study developed and validated monolingual and bilingual sentence-bidirectional encoder representations from transformers (SBERT) models for detecting cancer recurrence within Thai-English electronic medical records (EMRs) from Thai cancer hospitals.</p><p><strong>Method: </strong>A multicentre dataset of 32 436 documents from 1250 patients was used for model development. External validation involved an independent dataset of 9244 documents from 384 patients across two Thai cancer hospitals. Performance was benchmarked against a fine-tuned PubMedBERT (MetBERT).</p><p><strong>Results: </strong>The development dataset included breast (43.9%), colorectal (12.1%), cervical (28.0%) and head and neck (16.0%) cancers. MetBERT achieved the highest area under the precision-recall curve (AUPRC) for locoregional versus no recurrence (11.1%) and locoregional versus distant recurrence (91.7%), while monolingual-SBERT excelled at distant versus no recurrence (32.0%). External validation demonstrated MetBERT superiority for locoregional versus no recurrence (9.30%-21.50%). For distant versus no recurrence, bilingual-SBERT performed best with AUPRC 17.55%-24.39%. While MetBERT led in distinguishing locoregional versus distant recurrence (88.30%-94.70%), bilingual-SBERT demonstrated robust external validation performance (AUPRC 85.25%-91.80%).</p><p><strong>Discussion: </strong>Low AUPRC values (9%-32%) reflect the extreme class imbalance in real-world data (~1% recurrence prevalence). Despite this, fine-tuned MetBERT achieved highest performance, while bilingual-SBERT demonstrated superior robustness during external validation. This validates sentence embedding models for handling mixed Thai-English medical records in multilingual clinical environments.</p><p><strong>Conclusion: </strong>Sentence embedding frameworks provide a practical, generalisable solution for detecting cancer recurrence within multilingual EMRs. Despite text-length constraints, these models are suitable for clinical integration as a screening tool for cancer registry workflows.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13330879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148374497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting health and disease: a conceptual framework for AI in preventive and precision medicine.","authors":"Anis Barmada","doi":"10.1136/bmjhci-2025-101798","DOIUrl":"10.1136/bmjhci-2025-101798","url":null,"abstract":"<p><p>The conventional medical approach of treating symptoms as they appear with restricted screening often limits intervention to slowing disease progression rather than fully reversing it. A new approach leveraging artificial intelligence (AI) and computational technologies across expanding multimodal biomedical datasets holds the promise to enable predicting actionable future changes in health before symptom onset. This article presents a conceptual framework for a preventive paradigm in precision medicine and healthcare, integrating recent advancements in AI and biomedical datasets. Key remaining challenges facing computational systems, real-world clinical validation and implementation, and preventive interventions together with recommendations and prioritised future directions are highlighted. Such an approach could pave the way for more proactive and preventive medical interventions to effectively address the growing burden of chronic disease.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148374562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ambient AI in primary care: an exploratory mixed methods survey of UK general practitioners.","authors":"Charlotte Blease, Anna Kharko, Carolina Garcia Sanchez, David Navarro, Brian McMillan, Jens Gaab, Cosima Locher, Enrico Coiera","doi":"10.1136/bmjhci-2025-101847","DOIUrl":"10.1136/bmjhci-2025-101847","url":null,"abstract":"<p><strong>Objective: </strong>To examine UK general practitioners' (GPs) adoption of ambient artificial intelligence (AI) scribes and to assess user-reported error rates, workflow impact and consent practices in primary care.</p><p><strong>Methods: </strong>We conducted a nationwide online mixed-methods survey of GPs recruited via Doctors.net.uk. Detailed analyses of use, errors and workflow impact focused on current users of ambient AI scribes.</p><p><strong>Results: </strong>In August 2025, of 1003 respondents, 14% (n=141) reported current use of ambient AI scribes, 39% (n=396) intended to adopt them soon and 46% (n=466) had no plans to use them. Among users (n=141), Heidi Health predominated (86%). Most reported efficiency gains: 80% (n=112) reported reduced time spent on documentation and 70% (n=99) reduced cognitive load. Documentation quality was judged positively, with 55% (n=78) rating outputs as better than standard notes. Errors were common but usually minor: 32% (n=45) reported errors often/always, including 14% (n=20) with significant-to-critical implications. Errors were most frequent in multiparty consultations (38%), complex histories (35%) and non-English encounters (31%). Consent practices varied: 63% (n=89) routinely sought consent, with ≤10% of patients declining. Free-text responses (21% of users) highlighted benefits for workflow, alongside concerns about accuracy, ethics and system integration.</p><p><strong>Discussion: </strong>Findings suggest that ambient AI scribes deliver meaningful efficiency gains and improved perceived documentation quality, but introduce non-trivial risks related to accuracy, equity and medicolegal accountability. The uneven performance in complex and multilingual consultations raises particular concerns about potential exacerbation of healthcare disparities.</p><p><strong>Conclusion: </strong>Ambient AI scribes are already in use across UK primary care. Proactive regulation, consistent consent practices and independent evaluation, including patient perspectives, are urgently needed to ensure safe, equitable and sustainable implementation.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13331185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148366791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterising 'Watch and Wait' prescribing patterns in paediatric otitis media using large language models and pharmacy dispense data.","authors":"Jessica Pourian, Ben Michaels, Anh Vo, A Jay Holmgren, Augusto Garcia-Agundez, Valerie Flaherman","doi":"10.1136/bmjhci-2025-101948","DOIUrl":"10.1136/bmjhci-2025-101948","url":null,"abstract":"<p><strong>Objectives: </strong>Acute otitis media (AOM) is a leading cause of antibiotic prescribing in children although many cases resolve without treatment. 'Watch and wait' or safety-net antibiotic prescriptions (SNAPs) are recommended to support antibiotic stewardship. However, SNAP use and dispensing patterns are not well captured in structured data. This study evaluated SNAP prescribing, dispensing and associated demographic and clinical factors using a large language model (LLM).</p><p><strong>Methods: </strong>Retrospective cross-sectional study of 4370 AOM encounters (ages 6 months-<18 years) from 1 January 2021 to 1 January 2024 at a single academic centre, including emergency and outpatient visits. An LLM reviewed clinician notes to classify prescriptions as immediate ('treatment today prescriptions' (TTPs)) or delayed (SNAP). Dispensing data were linked from Epic Clarity.</p><p><strong>Results: </strong>Of 4370 encounters, 76.6% received TTP and 23.4% received SNAPs. Patients who spoke a language other than English and those in lower socio-economic quartiles were less likely to receive SNAPs (eg, average marginal effect (AME) = -8.0%, p<0.001). Boys were also less likely to receive SNAPs. SNAPs were less likely to be filled than TTPs (AME = -12.1%, p<0.001). Among patients who received a SNAP, time to dispensing did not differ significantly across demographic or clinical factors.</p><p><strong>Conclusion: </strong>SNAP prescribing varied across demographic and clinical groups, while dispensing patterns among SNAP recipients were similar across groups. These findings highlight variability in SNAP use and underscore the need for further research to better understand factors influencing prescribing and to support equitable antibiotic stewardship.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13289100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated monitoring in clinical and operational workflows.","authors":"Ju-Chuan Yen, Chia-An Hsu, Min-Huei Hsu","doi":"10.1136/bmjhci-2026-102219","DOIUrl":"10.1136/bmjhci-2026-102219","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13289137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.","authors":"Patrice Degoulet, Edward Hance Shortliffe, Christian Lovis, William Ed Hammond, Heimar de Fatima Marin, Yu-Chuan Jack Li, Otto Rienhoff, Brigitte Séroussi","doi":"10.1136/bmjhci-2026-102101","DOIUrl":"10.1136/bmjhci-2026-102101","url":null,"abstract":"<p><p>The rise of digital health (DH) has sparked debates about its relationship with the long-established field of biomedical and health informatics (BMHI), with some questioning the need for new academic programmes in DH since much of the likely content is already covered in BMHI training programmes. At the International Medical Informatics Association panel, 'Shaping the Future of Biomedical and Health Informatics in the Digital Health Era' (MEDINFO 2025), only one-third of participants clearly distinguished the two fields, highlighting conceptual ambiguities and the influence of DH narratives. This article compiles the perspectives expressed by the panellists, all experienced BMHI scholars, presenting their distinct viewpoints on how the field can maintain its scientific identity while engaging with emerging DH initiatives.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13288684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping of mental health indicators in the WHO European region: a scoping review.","authors":"Huda Abdul Ghaffar, Keyrellous Adib, Cassie Redlich, Mariken Tijhuis, Floor Boekelman, Nynke Koopman, Jippe Miedema, Ledia Lazeri, David Novillo-Ortiz","doi":"10.1136/bmjhci-2025-101907","DOIUrl":"10.1136/bmjhci-2025-101907","url":null,"abstract":"<p><strong>Objective: </strong>This scoping review aims to map and describe mental health indicators used in peer-reviewed studies in the WHO European Region to inform the development of a regional mental health measurement framework.</p><p><strong>Methods: </strong>reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR), this scoping review analysed 75 studies identified through database searches on mental health monitoring and indicators in the WHO European Region. The scope was restricted to English-language, peer-reviewed literature published from 2019 onwards. Indicators were extracted and standardised to reflect their original reported meaning, treated as distinct and pragmatically classified into nine predefined domains.</p><p><strong>Results: </strong>Across the 75 studies, 450 distinct indicators were identified. Most indicators related to mental health status and mental health risk factors and/or determinants. Geographic coverage varied, with northern and western European countries most frequently represented among the included literature. Diverse measurement tools were employed.</p><p><strong>Discussion: </strong>The predominance of indicators related to mental health status and determinants reflects the emphasis on burden and underlying factors, while system-level or structural indicators remain under-represented. Differences in geographic coverage likely stem from disparities in research capacity, publication practices and data availability.</p><p><strong>Conclusions: </strong>This scoping review provides a descriptive overview of mental health indicators applied in recent peer-reviewed research in the Region. Greater alignment between research indicators and policy frameworks may strengthen the comparability and policy utility of mental health data across the Region.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13288971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring performance trajectories in lung cancer surgery: a longitudinal study using the French national hospital database from 2020 to 2024.","authors":"Alain Bernard, Jonathan Cottenet, Pascale Tubert-Bitter, Catherine Quantin","doi":"10.1136/bmjhci-2025-101986","DOIUrl":"10.1136/bmjhci-2025-101986","url":null,"abstract":"<p><strong>Objectives: </strong>Lung cancer surgery is associated with significant postoperative morbidity (15%-40%) and mortality (2%-5%), with considerable interhospital variability. This study conducts the first national longitudinal analysis of performance trajectories in lung cancer surgery using advanced trajectory modelling approaches to understand temporal dynamics of quality evolution across hospitals.</p><p><strong>Methods: </strong>We included all adult patients (French national hospital database, n=56 299; 148 hospitals) who underwent lung resection for primary lung cancer in 2020-2024. The primary outcome was severe complications including major postoperative complications and 30-day mortality. Annual logistic regression models incorporating clinical and procedural variables calculated risk-adjusted rates (RAR) for each hospital. Hospitals were classified using funnel plots corrected for overdispersion. Hidden Markov Models estimated transition probabilities between performance categories, while group-based multitrajectory models identified subgroups of hospitals sharing similar evolution profiles, stratified by activity volume (low, <43; medium, 43-100; high, >100 procedures/year).</p><p><strong>Results: </strong>Predictive models demonstrated excellent stable discrimination (mean area under the ROC curve=0.890). Markov models revealed strong performance inertia (persistence probabilities ≥78%). Trajectory analysis identified a complex non-linear relationship between volume and quality. Medium-volume hospitals showed optimal improvement trajectories (45.4-45.8% RAR reduction). Conversely, some low-volume centres experienced catastrophic deterioration (843% RAR increase), while paradoxically, a subgroup of high-volume hospitals also showed concerning performance degradation (151% increase).</p><p><strong>Discussion: </strong>This study demonstrates heterogeneous performance trajectories in French lung cancer surgery, challenging the assumption that high-volume alone guarantees quality and suggesting organisational complexity challenges.</p><p><strong>Conclusion: </strong>This study shows the value of implementing systematic outcome monitoring using existing administrative data to target quality improvement interventions and reduce inter-hospital performance variations.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13295999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alzheimer's disease risk prediction from clinical and social determinants of health: a machine learning cohort study in UK Biobank.","authors":"Junming Hu, Simon Lu, Qi Zhang, Kehan Qian, Habbiburr Rehman, Congcong Zhu, John Farrell, Julio E Castrillon-Candas, Rhoda Au, Lindsay A Farrer, Wei Q Qiu, Jinying Chen, Xiaoling Zhang","doi":"10.1136/bmjhci-2025-101803","DOIUrl":"10.1136/bmjhci-2025-101803","url":null,"abstract":"<p><strong>Objectives: </strong>Social determinants of health (SDOH) may improve Alzheimer's disease (AD) risk prediction by capturing upstream contextual risk beyond routinely measured clinical variables. We aimed to develop and validate an accurate, interpretable machine-learning pipeline for AD risk prediction in UK Biobank using routinely collected data.</p><p><strong>Methods: </strong>Using data from 13 076 participants in the UK Biobank, we developed an automated machine-learning pipeline for AD risk prediction with feature selection and a C5.0 boosted-tree classifier. Data were split into training, development and test sets (7:2:1); missing values were imputed in the training data only, and feature selection, tuning and threshold calibration were performed using the training/development data, with final evaluation on the independent test set. Internal validation used repeated subsampling without replacement.</p><p><strong>Results: </strong>During up to 16 years of follow-up, 927 participants developed AD. Feature selection reduced 3590 variables to 26 predictors spanning age, APOE4, SDOH, medical history and routine clinical measures. The final model showed good discrimination (area under the precision-recall curve 0.89) and adequate calibration (Hosmer-Lemeshow p=0.71), with stable performance under repeated subsampling. Sex-stratified models showed similar patterns.</p><p><strong>Discussion: </strong>SDOH contributed useful predictive information, but their associations should be interpreted as predictive rather than causal and may reflect socioeconomic confounding and healthcare access.</p><p><strong>Conclusions: </strong>This model could support scalable AD risk screening using routinely collected data, but external validation and recalibration in non-UK populations are needed before broader application.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13288690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148300841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}