{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T22:59:48Z","timestamp":1778799588483,"version":"3.51.4"},"reference-count":44,"publisher":"BMJ","issue":"12","license":[{"start":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T00:00:00Z","timestamp":1766448000000},"content-version":"unspecified","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SciLifeLab & Wallenberg Data Driven Life Science Program","award":["KAW 2024.0159"],"award-info":[{"award-number":["KAW 2024.0159"]}]},{"name":"Karolinska Institute Research Foundation"},{"DOI":"10.13039\/501100007083","name":"Orion Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007083","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004047","name":"Karolinska Institutet","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100032253","name":"Nordic Cancer Union","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100032253","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Region Stockholm"},{"name":"David and Astrid H\u00e4gelen Foundation"},{"name":"Oskar Huttunen Foundation"},{"DOI":"10.13039\/501100007232","name":"Radiumhemmets Forskningsfonder","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007232","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009450","name":"KAUTE Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009450","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100008413","name":"Instrumentarium Science Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008413","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Swedish Cancer Society","award":["22 2324 Pj 01 H"],"award-info":[{"award-number":["22 2324 Pj 01 H"]}]},{"name":"Swedish Cancer Society","award":["23 3256 S 01 H"],"award-info":[{"award-number":["23 3256 S 01 H"]}]},{"name":"ITEA-Vinnova","award":["2022-01275"],"award-info":[{"award-number":["2022-01275"]}]},{"name":"ITEA-Vinnova","award":["Symphony"],"award-info":[{"award-number":["Symphony"]}]},{"name":"Swedish Prostate Cancer Society"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,12,2]]},"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass other critical diagnostic and prognostic markers, including cancer volume quantification and detection of cribriform morphology patterns and perineural invasion. Artificial intelligence (AI) applications in digital pathology have emerged as promising solutions for enhancing diagnostic consistency and accuracy, with recent research demonstrating that automated systems can match expert-level performance in prostate biopsy evaluation. Nevertheless, comprehensive validation studies have revealed concerning limitations in model generalisability when deployed across different clinical environments and patient populations. Recent systematic reviews revealed widespread risk-of-bias limitations and insufficient external validation in AI diagnostic studies, highlighting critical needs for accumulated evidence supporting generalisability before clinical implementation. Rigorous external validation with preregistered protocols using independent datasets from diverse clinical settings remains essential to establish the reliability and safety of AI-assisted prostate pathology systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>This study protocol establishes a framework for the retrospective external validation of an AI system developed for prostate biopsy assessment, to be conducted on the case-control samples of the National Prostate Cancer Register of Sweden, ProMort study (1998-2015). The primary aim is to evaluate the AI model\u2019s diagnostic accuracy and Gleason grading performance using completely independent datasets separate from any model development or previously used validation cohorts. The diversity of the validation samples, spanning multiple geographic regions, temporal collection periods and reference standards, allows evaluation of model robustness across varied clinical contexts. Secondary aims encompass evaluating AI performance in cancer length estimation and detection of cribriform patterns and perineural invasion. This protocol delineates procedures for data collection, reference standard clarification and prespecified statistical analyses, ensuring comprehensive validation and reliable performance assessment. The study design conforms to established reporting guidelines Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Standards for Reporting Diagnostic Accuracy Studies using Artificial Intelligence (STARD-AI), and recognised best practices for AI validation in medical imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>Data collection and usage were approved by the Swedish Regional Ethics Review Board and the Swedish Ethical Review Authority (permits 2012\/1586-31\/1, 2016\/613-31\/2, 2019-01395, 2019-05220). The study adheres to the Declaration of Helsinki principles, and findings will be made available in open access peer-reviewed publications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2025-111361","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T06:10:52Z","timestamp":1766643052000},"page":"e111361","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies on the ProMort cohort: study protocol"],"prefix":"10.1136","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8431-7906","authenticated-orcid":false,"given":"Xiaoyi","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renata","family":"Zelic","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden"},{"name":"Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oskar","family":"Aspegren","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden"},{"name":"Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden"},{"name":"Department of Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nita","family":"Mulliqi","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelangelo","family":"Fiorentino","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Giunchi","sequence":"additional","affiliation":[{"name":"Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna Policlinico di Sant\u2019Orsola, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Molinaro","sequence":"additional","affiliation":[{"name":"Division of Pathology, AOU Citt\u00e0 Della Salute e Della Scienza di Torino, Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sol Erika","family":"Boman","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"},{"name":"Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0554-1872","authenticated-orcid":false,"given":"Kelvin","family":"Szolnoky","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luana Xuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Pettersson","sequence":"additional","affiliation":[{"name":"Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden"},{"name":"Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Per Henrik","family":"Vincent","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden"},{"name":"Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Eklund","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olof","family":"Akre","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden"},{"name":"Department of Pelvic Cancer, Cancer Theme, Karolinska University Hospital, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9470-4783","authenticated-orcid":false,"given":"Kimmo","family":"Kartasalo","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, SciLifeLab, Karolinska Institutet, Stockholm, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"2026010703483390000_15.12.e111361.1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5347(17)59889-4"},{"key":"2026010703483390000_15.12.e111361.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2009.01.012"},{"key":"2026010703483390000_15.12.e111361.3","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/21681805.2016.1206619","article-title":"Interobserver variability in Gleason histological grading of prostate cancer","volume":"50","author":"Ozkan","year":"2016","journal-title":"Scand J Urol"},{"key":"2026010703483390000_15.12.e111361.4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/his.12008","article-title":"Standardization of Gleason grading among 337 European pathologists","volume":"62","author":"Egevad","year":"2013","journal-title":"Histopathology"},{"key":"2026010703483390000_15.12.e111361.5","doi-asserted-by":"crossref","unstructured":"Epstein JI , Allsbrook WC , Amin MB , et al . ISUP grading committee. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol; September 2005:1228\u201342. doi:10.1097\/01.pas.0000173646.99337.b1","DOI":"10.1097\/01.pas.0000173646.99337.b1"},{"key":"2026010703483390000_15.12.e111361.6","doi-asserted-by":"crossref","unstructured":"The 2014 international society of urological pathology (isup) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. The American journal of surgical pathology; 2016:244\u201352. doi:10.1097\/PAS.0000000000000530","DOI":"10.1097\/PAS.0000000000000530"},{"key":"2026010703483390000_15.12.e111361.7","unstructured":"Grignon DJ , et al . The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma; August 2020:e87\u201399."},{"key":"2026010703483390000_15.12.e111361.8","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1111\/his.14867","article-title":"Interobserver reproducibility of cribriform cancer in prostate needle biopsies and validation of International Society of Urological Pathology criteria","volume":"82","author":"Egevad","year":"2023","journal-title":"Histopathology"},{"key":"2026010703483390000_15.12.e111361.9","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s00428-021-03039-z","article-title":"Interobserver reproducibility of perineural invasion of prostatic adenocarcinoma in needle biopsies","volume":"478","author":"Egevad","year":"2021","journal-title":"Virchows Arch"},{"key":"2026010703483390000_15.12.e111361.10","first-page":"664","article-title":"Comparison of two commonly used methods in measurement of cancer volume in prostate biopsy","volume":"13","author":"Patel","year":"2020","journal-title":"Int J Clin Exp Pathol"},{"key":"2026010703483390000_15.12.e111361.11","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1111\/his.15372","article-title":"Unexpectedly high variability in determining tumour extent in prostatic biopsies: implications for active surveillance","volume":"86","author":"Bernhardt","year":"2025","journal-title":"Histopathology"},{"key":"2026010703483390000_15.12.e111361.12","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0508-1"},{"key":"2026010703483390000_15.12.e111361.13","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30738-7"},{"key":"2026010703483390000_15.12.e111361.14","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30739-9"},{"key":"2026010703483390000_15.12.e111361.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01620-2"},{"key":"2026010703483390000_15.12.e111361.16","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.5858\/arpa.2022-0066-OA","article-title":"Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection","volume":"147","author":"Raciti","year":"2023","journal-title":"Arch Pathol Lab Med"},{"key":"2026010703483390000_15.12.e111361.17","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3"},{"key":"2026010703483390000_15.12.e111361.18","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0"},{"key":"2026010703483390000_15.12.e111361.19","unstructured":"Zimmermann E , Vorontsov E , Viret J , et al . Virchow2: Scaling self-supervised mixed magnification models in pathology. arXiv 2024. Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.2408.00738"},{"key":"2026010703483390000_15.12.e111361.20","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4"},{"key":"2026010703483390000_15.12.e111361.21","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-024-01294-3","article-title":"Public evidence on AI products for digital pathology","volume":"7","author":"Matthews","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026010703483390000_15.12.e111361.22","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-024-01106-8","article-title":"Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy","volume":"7","author":"McGenity","year":"2024","journal-title":"NPJ Digit Med"},{"key":"2026010703483390000_15.12.e111361.23","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-024-03122-5","article-title":"Registered multi-device\/staining histology image dataset for domain-agnostic machine learning models","volume":"11","author":"Ochi","year":"2024","journal-title":"Sci Data"},{"key":"2026010703483390000_15.12.e111361.24","doi-asserted-by":"crossref","first-page":"100715","DOI":"10.1016\/j.modpat.2025.100715","article-title":"Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence-Assisted Cancer Diagnosis","volume":"38","author":"Ji","year":"2025","journal-title":"Mod Pathol"},{"key":"2026010703483390000_15.12.e111361.25","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-82911-z","article-title":"Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer","volume":"11","author":"Zelic","year":"2021","journal-title":"Sci Rep"},{"key":"2026010703483390000_15.12.e111361.26","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-021-00469-6","article-title":"Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning","volume":"4","author":"Mun","year":"2021","journal-title":"NPJ Digit Med"},{"key":"2026010703483390000_15.12.e111361.27","doi-asserted-by":"crossref","unstructured":"Guan H , Bates D , Zhou L . Keeping medical AI healthy: A review of detection and correction methods for system degradation. arXiv 2025. Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.2506.17442","DOI":"10.1109\/TBME.2025.3642706"},{"key":"2026010703483390000_15.12.e111361.28","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2022.100091","article-title":"Reporting of Artificial Intelligence Diagnostic Accuracy Studies in Pathology Abstracts: Compliance with STARD for Abstracts Guidelines","volume":"13","author":"McGenity","year":"2022","journal-title":"J Pathol Inform"},{"key":"2026010703483390000_15.12.e111361.29","unstructured":"Mulliqi N , Blilie A , Ji X , et al . Foundation models -- A panacea for artificial intelligence in pathology. arXiv 2025. Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.2502.21264"},{"key":"2026010703483390000_15.12.e111361.30","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2024-097591","article-title":"Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol","volume":"15","author":"Mulliqi","year":"2025","journal-title":"BMJ Open"},{"key":"2026010703483390000_15.12.e111361.31","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1093\/aje\/kwz026","article-title":"Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study","volume":"188","author":"Zelic","year":"2019","journal-title":"Am J Epidemiol"},{"key":"2026010703483390000_15.12.e111361.32","doi-asserted-by":"crossref","first-page":"59","DOI":"10.2147\/CLEP.S339140","article-title":"Prognostic Utility of the Gleason Grading System Revisions and Histopathological Factors Beyond Gleason Grade","volume":"14","author":"Zelic","year":"2022","journal-title":"Clin Epidemiol"},{"key":"2026010703483390000_15.12.e111361.33","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol Artif Intell"},{"key":"2026010703483390000_15.12.e111361.34","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat Med"},{"key":"2026010703483390000_15.12.e111361.35","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2016-012799","article-title":"STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration","volume":"6","author":"Cohen","year":"2016","journal-title":"BMJ Open"},{"key":"2026010703483390000_15.12.e111361.36","doi-asserted-by":"crossref","DOI":"10.1038\/s43856-025-01185-y","article-title":"Artificial intelligence-assisted prostate cancer diagnosis for reduced use of immunohistochemistry","volume":"5","author":"Blilie","year":"2025","journal-title":"Commun Med (Lond)"},{"key":"2026010703483390000_15.12.e111361.37","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(15)00361-7"},{"key":"2026010703483390000_15.12.e111361.38","unstructured":"Zhou Z , Siddiquee MMR , Tajbakhsh N , et al . n.d. UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.1807.10165"},{"key":"2026010703483390000_15.12.e111361.39","unstructured":"Abadi M , Agarwal A , Barham P , et al . TensorFlow: large-scale machine learning on heterogeneous distributed systems. n.d. Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.1603.04467"},{"key":"2026010703483390000_15.12.e111361.40","unstructured":"Ilse M , Tomczak J , Welling M . Attention-based deep multiple instance learning. 2018:2127\u201336."},{"key":"2026010703483390000_15.12.e111361.41","unstructured":"Tan M , Le QV . EfficientNet: Rethinking model scaling for convolutional Neural Networks, Available: http:\/\/dx.doi.org\/10.48550\/ARXIV.1905.11946"},{"key":"2026010703483390000_15.12.e111361.42","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-13546-7","article-title":"An open-source platform for structured annotation and computational workflows in digital pathology research","volume":"15","author":"Lianas","year":"2025","journal-title":"Sci Rep"},{"key":"2026010703483390000_15.12.e111361.43","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201200026"},{"key":"2026010703483390000_15.12.e111361.44","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.euo.2024.05.004","article-title":"Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study","volume":"8","author":"Du","year":"2025","journal-title":"Eur Urol Oncol"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2025-111361","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:52:02Z","timestamp":1767786722000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2025-111361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12,24]]},"published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1136\/bmjopen-2025-111361"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2025-111361","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"value":"2044-6055","type":"print"},{"value":"2044-6055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}