{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:42:44Z","timestamp":1770813764254,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2025ZD0547500"],"award-info":[{"award-number":["2025ZD0547500"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82200843"],"award-info":[{"award-number":["82200843"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSFC Incubation Project of Guangdong Provincial People's Hospital","award":["KY0120220048"],"award-info":[{"award-number":["KY0120220048"]}]},{"name":"Science and Technology Projects in Guangzhou","award":["2023B03J1250"],"award-info":[{"award-number":["2023B03J1250"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02323-5","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T12:25:10Z","timestamp":1768047910000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KT-LLM: an evidence-grounded and sequence text framework for auditable kidney transplant modeling"],"prefix":"10.1038","volume":"9","author":[{"given":"Haofeng","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihuan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiming","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangtianxu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyi","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jieyi","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingfu","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiquan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"2323_CR1","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.trre.2013.01.002","volume":"27","author":"S Leppke","year":"2013","unstructured":"Leppke, S. et al. Scientific registry of transplant recipients: collecting, analyzing, and reporting data on transplantation in the United States. Transplant. Rev. 27, 50\u201356 (2013).","journal-title":"Transplant. Rev."},{"key":"2323_CR2","doi-asserted-by":"crossref","unstructured":"Spadaccini, N., Hall, S. R. & Castleden, I. R. Relational expressions in star file dictionaries. J. Chem. Inf. Comput. Sci. 40, 1289\u20131301 (2000).","DOI":"10.1021\/ci000009k"},{"key":"2323_CR3","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1080\/01621459.1999.10474144","volume":"94","author":"JP Fine","year":"1999","unstructured":"Fine, J. P. & Gray, R. J. A proportional hazards model for the subdistribution of a competing risk. J. Am. Stat. Assoc. 94, 496\u2013509 (1999).","journal-title":"J. Am. Stat. Assoc."},{"key":"2323_CR4","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1097\/TP.0000000000002366","volume":"102","author":"C Roufosse","year":"2018","unstructured":"Roufosse, C. et al. A 2018 reference guide to the Banff classification of renal allograft pathology. Transplantation 102, 1795\u20131814 (2018).","journal-title":"Transplantation"},{"key":"2323_CR5","doi-asserted-by":"crossref","unstructured":"Loupy, A. et al. The Banff 2019 kidney meeting report (i): updates on and clarification of criteria for T cell\u2013and antibody-mediated rejection. Am. J. Transplant. 20, 2318\u20132331 (2020).","DOI":"10.1111\/ajt.15898"},{"key":"2323_CR6","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.ajt.2023.10.016","volume":"24","author":"M Naesens","year":"2024","unstructured":"Naesens, M. et al. The Banff 2022 kidney meeting report: reappraisal of microvascular inflammation and the role of biopsy-based transcript diagnostics. Am. J. Transplant. 24, 338\u2013349 (2024).","journal-title":"Am. J. Transplant."},{"key":"2323_CR7","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1111\/ajt.16974","volume":"22","author":"A Israni","year":"2022","unstructured":"Israni, A. Optn\/srtr 2020 annual data report: introduction. Am. J. Transplant. 22, 11\u201320 (2022).","journal-title":"Am. J. Transplant."},{"key":"2323_CR8","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1097\/TP.0000000000000799","volume":"99","author":"A Gupta","year":"2015","unstructured":"Gupta, A. et al. Program-specific reports: a guide to the debate. Transplantation 99, 1109\u20131112 (2015).","journal-title":"Transplantation"},{"key":"2323_CR9","unstructured":"Scientific Registry Of Transplant Recipients. Technical Methods for the Program-Specific Reports (SRTR, 2022)."},{"key":"2323_CR10","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1001\/jamainternmed.2025.0043","volume":"185","author":"L Myaskovsky","year":"2025","unstructured":"Myaskovsky, L. et al. Kidney transplant fast track and likelihood of waitlisting and transplant: a nonrandomized clinical trial. JAMA Intern. Med. 185, 499\u2013509 (2025).","journal-title":"JAMA Intern. Med."},{"key":"2323_CR11","doi-asserted-by":"crossref","unstructured":"Singh, T. P. et al. Graft survival in primary thoracic organ transplant recipients: A special report from the International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation. J. Heart Lung Transplant. 42, 1321\u20131333 (2023).","DOI":"10.1016\/j.healun.2023.07.017"},{"key":"2323_CR12","doi-asserted-by":"crossref","unstructured":"VanWagner, L. B. & Skaro, A. I. Program-specific reports: implications and impact on program behavior. Curr. Opin. Organ Transplant. 18, 210\u2013215 (2013).","DOI":"10.1097\/MOT.0b013e32835f07f8"},{"key":"2323_CR13","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.kint.2021.11.028","volume":"101","author":"A Loupy","year":"2022","unstructured":"Loupy, A., Mengel, M. & Haas, M. Thirty years of the international banff classification for allograft pathology: the past, present, and future of kidney transplant diagnostics. Kidney Int. 101, 678\u2013691 (2022).","journal-title":"Kidney Int."},{"key":"2323_CR14","doi-asserted-by":"crossref","unstructured":"Haas, M. et al. The Banff 2017 kidney meeting report: Revised diagnostic criteria for chronic active T cell-mediated rejection, antibody-mediated rejection, and prospects for integrative endpoints for next-generation clinical trials. Am. J. Transplant. 18, 293\u2013307 (2018).","DOI":"10.1111\/ajt.14625"},{"key":"2323_CR15","doi-asserted-by":"crossref","unstructured":"Farris, A. B. et al. Banff digital pathology working group: going digital in transplant pathology. Am. J. Transplant. 20, 2392\u20132399 (2020).","DOI":"10.1111\/ajt.15850"},{"key":"2323_CR16","doi-asserted-by":"crossref","unstructured":"Farris, A. B. et al. Banff digital pathology working group: image bank, artificial intelligence algorithm, and challenge trial developments. Transpl. Int. 36, 11783 (2023).","DOI":"10.3389\/ti.2023.11783"},{"key":"2323_CR17","doi-asserted-by":"publisher","first-page":"2994","DOI":"10.1681\/ASN.2021070988","volume":"32","author":"C Delgado","year":"2021","unstructured":"Delgado, C. et al. A unifying approach for GFR estimation: recommendations of the NKF-ASN task force on reassessing the inclusion of race in diagnosing kidney disease. J. Am. Soc. Nephrol. 32, 2994\u20133015 (2021).","journal-title":"J. Am. Soc. Nephrol."},{"key":"2323_CR18","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1056\/NEJMoa2102953","volume":"385","author":"LA Inker","year":"2021","unstructured":"Inker, L. A. et al. New creatinine-and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737\u20131749 (2021).","journal-title":"N. Engl. J. Med."},{"key":"2323_CR19","doi-asserted-by":"publisher","first-page":"e221286","DOI":"10.1001\/jamasurg.2022.1286","volume":"157","author":"C Thongprayoon","year":"2022","unstructured":"Thongprayoon, C. et al. Use of machine learning consensus clustering to identify distinct subtypes of black kidney transplant recipients and associated outcomes. JAMA Surg. 157, e221286\u2013e221286 (2022).","journal-title":"JAMA Surg."},{"key":"2323_CR20","unstructured":"For Organ Sharing (UNOS), U. N. et al. Implementation Notice: Requirement for Race-Neutral eGFR Formulas in Effect (UNOS, 2023)."},{"key":"2323_CR21","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1097\/LVT.0000000000000410","volume":"31","author":"MA Fallahzadeh","year":"2025","unstructured":"Fallahzadeh, M. A. et al. Performance of race-neutral eGFR equations in patients with decompensated cirrhosis. Liver Transplant. 31, 170\u2013180 (2025).","journal-title":"Liver Transplant."},{"key":"2323_CR22","unstructured":"Procurement, O. & Network, T. Modify Waiting Time for Candidates Affected by Race-Inclusive Estimated Glomerular Filtration Rate (eGFR) Calculations (HRSA, 2023)."},{"key":"2323_CR23","unstructured":"Procurement, O. & Network, T. Waiting Time Modifications for Candidates Affected by Race-Inclusive eGFR Calculations (HRSA, 2024)."},{"key":"2323_CR24","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. Ser. B 34, 187\u2013202 (1972).","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"2323_CR25","doi-asserted-by":"crossref","unstructured":"Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests (2008).","DOI":"10.1214\/08-AOAS169"},{"key":"2323_CR26","doi-asserted-by":"crossref","unstructured":"Lee, C., Zame, W., Yoon, J. & Van Der Schaar, M. Deephit: a deep learning approach to survival analysis with competing risks. In Proc. the AAAI Conference on Artificial Intelligence, Vol. 32 (PKP Publishing Services Network, 2018).","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"2323_CR27","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J. L. et al. Deepsurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 24 (2018).","journal-title":"BMC Med. Res. Methodol."},{"key":"2323_CR28","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","volume":"56","author":"PJ Heagerty","year":"2000","unstructured":"Heagerty, P. J., Lumley, T. & Pepe, M. S. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337\u2013344 (2000).","journal-title":"Biometrics"},{"key":"2323_CR29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1111\/j.0006-341X.2005.030814.x","volume":"61","author":"PJ Heagerty","year":"2005","unstructured":"Heagerty, P. J. & Zheng, Y. Survival model predictive accuracy and ROC curves. Biometrics 61, 92\u2013105 (2005).","journal-title":"Biometrics"},{"key":"2323_CR30","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5","volume":"18","author":"E Graf","year":"1999","unstructured":"Graf, E., Schmoor, C., Sauerbrei, W. & Schumacher, M. Assessment and comparison of prognostic classification schemes for survival data. Stat. Med. 18, 2529\u20132545 (1999).","journal-title":"Stat. Med."},{"key":"2323_CR31","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1002\/bimj.200610301","volume":"48","author":"TA Gerds","year":"2006","unstructured":"Gerds, T. A. & Schumacher, M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical J. 48, 1029\u20131040 (2006).","journal-title":"Biometrical J."},{"key":"2323_CR32","unstructured":"Vaswani, A. et al. Attention is all you need. Advances in Neural Information Processing Systems 30 (NIPS, 2017)."},{"key":"2323_CR33","doi-asserted-by":"crossref","unstructured":"Dai, Z. et al. Transformer-xl: attentive language models beyond a fixed-length context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL, 2019).","DOI":"10.18653\/v1\/P19-1285"},{"key":"2323_CR34","unstructured":"Zaheer, M. et al. Big Bird: transformers for longer sequences. Comput. Sci. 33, 17283\u201317297 (2020)."},{"key":"2323_CR35","unstructured":"Choromanski, K. et al. Rethinking attention with performers. The 9th International Conference on Learning Representations (ICLR, 2021)."},{"key":"2323_CR36","unstructured":"Gu, A. & Dao, T. Mamba: linear-time sequence modeling with selective state spaces. In First Conference on Language Modeling (COLM, 2024)."},{"key":"2323_CR37","unstructured":"Gu, A., Goel, K. & R\u00e9, C. Efficiently modeling long sequences with structured state spaces. The 10th International Conference on Learning Representations (ICLR 2022)."},{"key":"2323_CR38","unstructured":"Lewis, P. et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Comput. Sci. 33, 9459\u20139474 (2020)."},{"key":"2323_CR39","doi-asserted-by":"crossref","unstructured":"Karpukhin, V. et al. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP, 2020).","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"2323_CR40","doi-asserted-by":"crossref","unstructured":"Izacard, G. & Grave, E. Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (EACL, 2021).","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"2323_CR41","doi-asserted-by":"crossref","unstructured":"Petroni, F. et al. Kilt: a benchmark for knowledge intensive language tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL, 2021).","DOI":"10.18653\/v1\/2021.naacl-main.200"},{"key":"2323_CR42","unstructured":"Borgeaud, S. et al. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning, 2206\u20132240 (PMLR, 2022)."},{"key":"2323_CR43","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193\u2013218 (1985).","journal-title":"J. Classif."},{"key":"2323_CR44","unstructured":"Vinh, N., Epps, J. & Bailey, J. Information theoretic measures for clusterings comparison: Variants, Properties, normalization and correction for chance. J. Mach. Learn. Res. 18, 2837\u20132854 (2009)."},{"key":"2323_CR45","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1561\/1500000019","volume":"3","author":"S Robertson","year":"2009","unstructured":"Robertson, S., Zaragoza, H. et al. The probabilistic relevance framework: BM25 and beyond. Found. Trends\u00ae Inf. Retr. 3, 333\u2013389 (2009).","journal-title":"Found. Trends\u00ae Inf. Retr."},{"key":"2323_CR46","unstructured":"Izacard, G. et al. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research (TMLR, 2022)."},{"key":"2323_CR47","unstructured":"Wang, L. et al. Text embeddings by weakly-supervised contrastive pre-training. Preprint at https:\/\/arxiv.org\/abs\/2212.03533 (2022)."},{"key":"2323_CR48","doi-asserted-by":"crossref","unstructured":"Wang, L. et al. Improving text embeddings with large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024).","DOI":"10.18653\/v1\/2024.acl-long.642"},{"key":"2323_CR49","doi-asserted-by":"crossref","unstructured":"Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C. & Zaharia, M. Colbertv2: effective and efficient retrieval via lightweight late interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL, 2022).","DOI":"10.18653\/v1\/2022.naacl-main.272"},{"key":"2323_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu, Y. et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. 3, 1\u201323 (2021).","journal-title":"ACM Trans. Comput. Healthc."},{"key":"2323_CR51","doi-asserted-by":"publisher","first-page":"bbac409","DOI":"10.1093\/bib\/bbac409","volume":"23","author":"R Luo","year":"2022","unstructured":"Luo, R. et al. Biogpt: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinforma. 23, bbac409 (2022).","journal-title":"Brief. Bioinforma."},{"key":"2323_CR52","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","volume":"31","author":"K Singhal","year":"2025","unstructured":"Singhal, K. et al. Toward expert-level medical question answering with large language models. Nat. Med. 31, 943\u2013950 (2025).","journal-title":"Nat. Med."},{"key":"2323_CR53","doi-asserted-by":"crossref","unstructured":"Pradeep, R. et al. Squeezing water from a stone: a bag of tricks for further improving cross-encoder effectiveness for reranking. In European Conference on Information Retrieval 655\u2013670 (Springer, 2022).","DOI":"10.1007\/978-3-030-99736-6_44"},{"key":"2323_CR54","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E. J. et al. Lora: Low-rank adaptation of large language models. ICLR 1, 3 (2022).","journal-title":"ICLR"},{"key":"2323_CR55","doi-asserted-by":"crossref","unstructured":"Prentice, R. L. & Gloeckler, L. A. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 57\u201367 (1978).","DOI":"10.2307\/2529588"},{"key":"2323_CR56","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1002\/sim.2712","volume":"26","author":"H Putter","year":"2007","unstructured":"Putter, H., Fiocco, M. & Geskus, R. B. Tutorial in biostatistics: competing risks and multi-state models. Stat. Med. 26, 2389\u20132430 (2007).","journal-title":"Stat. Med."},{"key":"2323_CR57","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1093\/ije\/dyr213","volume":"41","author":"PK Andersen","year":"2012","unstructured":"Andersen, P. K., Geskus, R. B., de Witte, T. & Putter, H. Competing risks in epidemiology: possibilities and pitfalls. Int. J. Epidemiol. 41, 861\u2013870 (2012).","journal-title":"Int. J. Epidemiol."},{"key":"2323_CR58","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TBME.2019.2909027","volume":"67","author":"C Lee","year":"2019","unstructured":"Lee, C., Yoon, J. & Van Der Schaar, M. Dynamic-deephit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Trans. Biomed. Eng. 67, 122\u2013133 (2019).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2323_CR59","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1093\/bioinformatics\/btp088","volume":"25","author":"H Binder","year":"2009","unstructured":"Binder, H., Allignol, A., Schumacher, M. & Beyersmann, J. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25, 890\u2013896 (2009).","journal-title":"Bioinformatics"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02323-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02323-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02323-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:07:21Z","timestamp":1770779241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02323-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,10]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2323"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02323-5","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,10]]},"assertion":[{"value":"20 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All authors declare no financial or non-financial competing interests relevant to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable. This study exclusively utilizes de-identified datasets from public repositories.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"142"}}