{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:16:50Z","timestamp":1780420610239,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science","award":["218529\/Z\/19\/Z"],"award-info":[{"award-number":["218529\/Z\/19\/Z"]}]},{"name":"Baidu Scholarship"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402017"],"award-info":[{"award-number":["62402017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["L244063"],"award-info":[{"award-number":["L244063"]}]},{"name":"Xuzhou Scientific Technological Projects","award":["KC23143"],"award-info":[{"award-number":["KC23143"]}]},{"name":"Peking University Medicine plus X Pilot Program-Key Technologies R&D Project","award":["2024YXXLHGG007"],"award-info":[{"award-number":["2024YXXLHGG007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, creating potential for misuse and misunderstanding. To address this, our ClinicRealm benchmark systematically evaluates 15 GPT-style LLMs, 5 BERT-style models, and 11 traditional methods on unstructured clinical notes and structured Electronic Health Records (EHR) across predictive performance, reasoning, fairness, etc. Our findings reveal a significant shift: on clinical notes, leading zero-shot LLMs (e.g., DeepSeek-V3.1-Think, GPT-5) now decisively outperform finetuned BERT models. On structured EHRs, while specialized models excel with ample data, advanced LLMs demonstrate potent zero-shot capabilities, often surpassing conventional models in data-scarce settings. Notably, leading open-source LLMs match or exceed their proprietary counterparts. This provides compelling evidence that modern LLMs are competitive tools for clinical prediction, necessitating a re-evaluation of model selection strategies by health data scientists and developers.<\/jats:p>","DOI":"10.1038\/s41746-026-02539-z","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:18:13Z","timestamp":1775668693000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks"],"prefix":"10.1038","volume":"9","author":[{"given":"Yinghao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel O.","family":"Bernabeu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasha","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lequan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwei","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewen M.","family":"Harrison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liantao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"2539_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0103-9","volume":"6","author":"H Harutyunyan","year":"2019","unstructured":"Harutyunyan, H., Khachatrian, H., Kale, D. C., Ver Steeg, G. & Galstyan, A. Multitask learning and benchmarking with clinical time series data. Sci. Data 6, 96 (2019).","journal-title":"Sci. Data"},{"key":"2539_CR2","first-page":"833","volume":"34","author":"L Ma","year":"2020","unstructured":"Ma, L. et al. Concare: personalized clinical feature embedding via capturing the healthcare context. Proc. AAAI Conf. Artif. Intell. 34, 833\u2013840 (2020).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2539_CR3","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","volume":"83","author":"S Purushotham","year":"2018","unstructured":"Purushotham, S., Meng, C., Che, Z. & Liu, Y. Benchmarking deep learning models on large healthcare datasets. J. Biomed. Inform. 83, 112\u2013134 (2018).","journal-title":"J. Biomed. Inform."},{"key":"2539_CR4","doi-asserted-by":"publisher","first-page":"e52865","DOI":"10.2196\/52865","volume":"25","author":"B Mesk\u00f3","year":"2023","unstructured":"Mesk\u00f3, B. The impact of multimodal large language models on health care\u2019s future. J. Med. Internet Res. 25, e52865 (2023).","journal-title":"J. Med. Internet Res."},{"key":"2539_CR5","first-page":"28","volume":"2","author":"J Qian","year":"2024","unstructured":"Qian, J., Jin, Z., Zhang, Q., Cai, G. & Liu, B. A liver cancer question-answering system based on next-generation intelligence and the large model MedPalm 2. Int. J. Comput. Sci. Inf. Technol. 2, 28\u201335 (2024).","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"2539_CR6","doi-asserted-by":"publisher","first-page":"e45312","DOI":"10.2196\/45312","volume":"9","author":"A Gilson","year":"2023","unstructured":"Gilson, A. et al. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of large language models for medical education and knowledge assessment. JMIR Med. Educ. 9, e45312 (2023).","journal-title":"JMIR Med. Educ."},{"key":"2539_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-56989-2","volume":"16","author":"Q Chen","year":"2025","unstructured":"Chen, Q. et al. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nat. Commun. 16, 3280 (2025).","journal-title":"Nat. Commun."},{"key":"2539_CR8","unstructured":"Chen, C. et al. ClinicalBench: Can LLMs beat traditional ML models in clinical prediction? In Workshop on GenAI for Health: Potential, Trust and Policy Compliance (GenAI4Health), NeurIPS (2024)."},{"key":"2539_CR9","unstructured":"Lehman, E. et al. Do we still need clinical language models? In Conference on health, inference, and learning, 578\u2013597 (PMLR, 2023)."},{"key":"2539_CR10","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1093\/jamia\/ocaf038","volume":"32","author":"KE Brown","year":"2025","unstructured":"Brown, K. E. et al. Large language models are less effective at clinical prediction tasks than locally trained machine learning models. J. Am. Med. Inform. Assoc. 32, 811\u2013822 (2025).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2539_CR11","doi-asserted-by":"publisher","unstructured":"Bucher, M. J. J. & Martini, M. Fine-tuned\u2019small\u2019llms (still) significantly outperform zero-shot generative AI models in text classification. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2406.08660 (2024).","DOI":"10.48550\/arXiv.2406.08660"},{"key":"2539_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson, A. E. et al. Mimic-iv, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).","journal-title":"Sci. Data"},{"key":"2539_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A. E. et al. Mimic-iii, a freely accessible critical care database. Sci. Data 3, 1\u20139 (2016).","journal-title":"Sci. Data"},{"key":"2539_CR14","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1038\/s42256-020-0180-7","volume":"2","author":"L Yan","year":"2020","unstructured":"Yan, L. et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2, 283\u2013288 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"2539_CR15","unstructured":"Johnson, A. et al. Mimic-iv (version 3.1) https:\/\/physionet.org\/content\/mimiciv\/3.1\/ (2024)."},{"key":"2539_CR16","unstructured":"Johnson, A., Pollard, T., Horng, S., Celi, L. A. & Mark, R. Mimic-iv-note: Deidentified free-text clinical notes (version 2.2). https:\/\/physionet.org\/content\/mimic-iv-note\/2.2\/. (2023)."},{"key":"2539_CR17","doi-asserted-by":"publisher","first-page":"100951","DOI":"10.1016\/j.patter.2024.100951","volume":"5","author":"J Gao","year":"2024","unstructured":"Gao, J. et al. A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care. Patterns 5, 100951 (2024).","journal-title":"Patterns"},{"key":"2539_CR18","unstructured":"Zhu, Y., Wang, W., Gao, J. & Ma, L. Pyehr: A predictive modeling toolkit for electronic health records. https:\/\/github.com\/yhzhu99\/pyehr (2023)."},{"key":"2539_CR19","doi-asserted-by":"crossref","unstructured":"Wells, B. J., Chagin, K. M., Nowacki, A. S. & Kattan, M. W. Strategies for handling missing data in electronic health record derived data. Egems 1, 1035 (2013).","DOI":"10.13063\/2327-9214.1035"},{"key":"2539_CR20","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1093\/jamia\/ocac225","volume":"30","author":"Y Li","year":"2023","unstructured":"Li, Y., Wehbe, R. M., Ahmad, F. S., Wang, H. & Luo, Y. A comparative study of pretrained language models for long clinical text. J. Am. Med. Inform. Assoc. 30, 340\u2013347 (2023).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"2539_CR21","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. Catboost: unbiased boosting with categorical features. Adv. Neural Inform. Process. Syst. 31, 1035 (2018)."},{"key":"2539_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"2539_CR23","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system. In Proc. 22nd acm sigkdd international conference on knowledge discovery and data mining, 785\u2013794 (ACM, 2016).","DOI":"10.1145\/2939672.2939785"},{"key":"2539_CR24","doi-asserted-by":"publisher","unstructured":"Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1412.3555 (2014).","DOI":"10.48550\/arXiv.1412.3555"},{"key":"2539_CR25","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997).","journal-title":"Neural Comput."},{"key":"2539_CR26","first-page":"2","volume":"5","author":"LR Medsker","year":"2001","unstructured":"Medsker, L. R. et al. Recurrent neural networks. Des. Appl. 5, 2 (2001).","journal-title":"Des. Appl."},{"key":"2539_CR27","first-page":"825","volume":"34","author":"L Ma","year":"2020","unstructured":"Ma, L. et al. Adacare: explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration. Proc. AAAI Conf. Artif. Intell. 34, 825\u2013832 (2020).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2539_CR28","first-page":"715","volume":"35","author":"C Zhang","year":"2021","unstructured":"Zhang, C. et al. Grasp: Generic framework for health status representation learning based on incorporating knowledge from similar patients. Proc. AAAI Conf. Artif. Intell. 35, 715\u2013723 (2021).","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2539_CR29","doi-asserted-by":"publisher","unstructured":"Ma, L. et al. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. Patterns 4. https:\/\/doi.org\/10.1016\/j.patter.2023.100892 (2023).","DOI":"10.1016\/j.patter.2023.100892"},{"key":"2539_CR30","unstructured":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), (eds. Burstein, J., Doran, C. & Solorio, T.) 4171\u20134186. https:\/\/aclanthology.org\/N19-1423. (Association for Computational Linguistics, 2019)."},{"key":"2539_CR31","doi-asserted-by":"publisher","unstructured":"Huang, K., Altosaar, J. & Ranganath, R. Clinicalbert: modeling clinical notes and predicting hospital readmission. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1904.05342 (2019).","DOI":"10.48550\/arXiv.1904.05342"},{"key":"2539_CR32","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J. et al. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234\u20131240 (2020).","journal-title":"Bioinformatics"},{"key":"2539_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00742-2","volume":"5","author":"X Yang","year":"2022","unstructured":"Yang, X. et al. A large language model for electronic health records. NPJ Digit. Med. 5, 194 (2022).","journal-title":"NPJ Digit. Med."},{"key":"2539_CR34","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1, 9 (2019).","journal-title":"OpenAI blog"},{"key":"2539_CR35","doi-asserted-by":"publisher","unstructured":"Hurst, A. et al. Gpt-4o system card. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2410.21276 (2024).","DOI":"10.48550\/arXiv.2410.21276"},{"key":"2539_CR36","doi-asserted-by":"publisher","unstructured":"Team, G. et al. Gemma 3 technical report. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2503.19786 (2025).","DOI":"10.48550\/arXiv.2503.19786"},{"key":"2539_CR37","doi-asserted-by":"publisher","unstructured":"Yang, A. et al. Qwen2.5 technical report. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2412.15115 (2024).","DOI":"10.48550\/arXiv.2412.15115"},{"key":"2539_CR38","doi-asserted-by":"publisher","unstructured":"Liu, A. et al. Deepseek-v3 technical report. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2412.19437 (2024).","DOI":"10.48550\/arXiv.2412.19437"},{"key":"2539_CR39","unstructured":"DeepSeek. Deepseek v3.1 release. https:\/\/api-docs.deepseek.com\/news\/news250821 (2025)."},{"key":"2539_CR40","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. Bioinform. 23, bbac409 (2022).","journal-title":"Brief. Bioinform."},{"key":"2539_CR41","doi-asserted-by":"publisher","unstructured":"Chen, Z. et al. Meditron-70b: scaling medical pretraining for large language models. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2311.16079 (2023).","DOI":"10.48550\/arXiv.2311.16079"},{"key":"2539_CR42","unstructured":"Ankit Pal, M. S. Openbiollms: advancing open-source large language models for healthcare and life sciences. https:\/\/huggingface.co\/aaditya\/OpenBioLLM-Llama3-70B (2024)."},{"key":"2539_CR43","doi-asserted-by":"crossref","unstructured":"Labrak, Y. et al. Biomistral: a collection of open-source pretrained large language models for medical domains. In Findings of the association for computational linguistics: acl, 5848-5864 (Association for Computational Linguistics, 2024).","DOI":"10.18653\/v1\/2024.findings-acl.348"},{"key":"2539_CR44","doi-asserted-by":"crossref","unstructured":"Chen, J. et al. Huatuogpt-o1, towards medical complex reasoning with llms. In Findings of the association for computational linguistics: acl, 14552-14573 (Association for Computational Linguistics, 2025).","DOI":"10.18653\/v1\/2025.findings-acl.751"},{"key":"2539_CR45","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1038\/s41586-025-09422-z","volume":"645","author":"D Guo","year":"2025","unstructured":"Guo, D. et al. Deepseek-r1 incentivizes reasoning in llms through reinforcement learning. Nature 645, 633\u2013638 (2025).","journal-title":"Nature"},{"key":"2539_CR46","unstructured":"OpenAI. Introducing gpt-5. accessed 02 October 2025. https:\/\/openai.com\/index\/introducing-gpt-5\/ (2025)."},{"key":"2539_CR47","unstructured":"OpenAI. Introducing openai o3 mini. accessed 02 October 2025. https:\/\/openai.com\/index\/openai-o3-mini\/ (2025)."},{"key":"2539_CR48","doi-asserted-by":"crossref","unstructured":"Sui, Y., Zhou, M., Zhou, M., Han, S. & Zhang, D. Table meets llm: Can large language models understand structured table data? a benchmark and empirical study. In Proc. 17th ACM International Conference on Web Search and Data Mining, 645\u2013654 (ACM, 2024).","DOI":"10.1145\/3616855.3635752"},{"key":"2539_CR49","doi-asserted-by":"crossref","unstructured":"Jiang, J. et al. StructGPT: a general framework for large language model to reason over structured data. In Proc. of the 2023 Conference on Empirical Methods in Natural Language Processing, 9237-9251 (Association for Computational Linguistics, 2023).","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"2539_CR50","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med."},{"key":"2539_CR51","doi-asserted-by":"crossref","unstructured":"Zhang, S. et al. Rethinking human-ai collaboration in complex medical decision making: a case study in sepsis diagnosis. In Proc. 2024 CHI Conference on Human Factors in Computing Systems. 1\u201318 (ACM, 2024).","DOI":"10.1145\/3613904.3642343"},{"key":"2539_CR52","unstructured":"Hegselmann, S. et al. Tabllm: Few-shot classification of tabular data with large language models. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS) (PMLR, 2023)."},{"key":"2539_CR53","doi-asserted-by":"publisher","first-page":"104458","DOI":"10.1016\/j.jbi.2023.104458","volume":"144","author":"Y Ge","year":"2023","unstructured":"Ge, Y., Guo, Y., Yang, Y.-C., Al-Garadi, M. A. & Sarker, A. Few-shot learning for medical text: A systematic review. J. Biomed. Inform. 144, 104458 (2023).","journal-title":"J. Biomed. Inform."},{"key":"2539_CR54","doi-asserted-by":"crossref","unstructured":"Sivaraman, V., Bukowski, L. A., Levin, J., Kahn, J. M. & Perer, A. Ignore, trust, or negotiate: understanding clinician acceptance of AI-based treatment recommendations in health care. In Proc. 2023 CHI Conference on Human Factors in Computing Systems, CHI \u201923, 1\u201318 (Association for Computing Machinery, 2023).","DOI":"10.1145\/3544548.3581075"},{"key":"2539_CR55","doi-asserted-by":"crossref","unstructured":"Burgess, E. R. et al. Healthcare ai treatment decision support: Design principles to enhance clinician adoption and trust. In Proc. 2023 CHI Conference on Human Factors in Computing Systems, 1\u201319 (CHI, 2023).","DOI":"10.1145\/3544548.3581251"},{"key":"2539_CR56","doi-asserted-by":"publisher","unstructured":"Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1711.05101 (2017).","DOI":"10.48550\/arXiv.1711.05101"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02539-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02539-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02539-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:40:09Z","timestamp":1776148809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02539-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2539"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02539-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]},"assertion":[{"value":"18 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"319"}}