{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T01:49:29Z","timestamp":1778291369164,"version":"3.51.4"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"U.S. Department of Defense (DoD) Grant"},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["1K23AR082986-01A1"],"award-info":[{"award-number":["1K23AR082986-01A1"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02337-7","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:33:46Z","timestamp":1768926826000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Clinically-guided models or foundation models? predicting cervical spondylotic myelopathy from electronic health records"],"prefix":"10.1038","volume":"9","author":[{"given":"Salim","family":"Yakdan","sequence":"first","affiliation":[]},{"given":"Ben","family":"Warner","sequence":"additional","affiliation":[]},{"given":"Zoher","family":"Ghogawala","sequence":"additional","affiliation":[]},{"given":"Wilson Z.","family":"Ray","sequence":"additional","affiliation":[]},{"given":"Mohamad","family":"Bydon","sequence":"additional","affiliation":[]},{"given":"Michael P.","family":"Steinmetz","sequence":"additional","affiliation":[]},{"given":"Richard T.","family":"Griffey","sequence":"additional","affiliation":[]},{"given":"Randi","family":"Foraker","sequence":"additional","affiliation":[]},{"given":"Adam","family":"Wilcox","sequence":"additional","affiliation":[]},{"given":"Chenyang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Jacob K.","family":"Greenberg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"2337_CR1","doi-asserted-by":"publisher","first-page":"639","DOI":"10.3171\/2024.4.SPINE24107","volume":"41","author":"JK Zhang","year":"2024","unstructured":"Zhang, J. K. et al. Spinal cord metrics derived from diffusion MRI: improvement in prognostication in cervical spondylotic myelopathy compared with conventional MRI. J. Neurosurg. Spine 41, 639\u2013647 (2024).","journal-title":"J. Neurosurg. Spine"},{"key":"2337_CR2","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41582-019-0303-0","volume":"16","author":"JH Badhiwala","year":"2020","unstructured":"Badhiwala, J. H. et al. Degenerative cervical myelopathy \u2014 update and future directions. Nat. Rev. Neurol. 16, 108\u2013124 (2020).","journal-title":"Nat. Rev. Neurol."},{"key":"2337_CR3","first-page":"1064","volume":"62","author":"WF Young","year":"2000","unstructured":"Young, W. F. Cervical spondylotic myelopathy: a common cause of spinal cord dysfunction in older persons. Am. Fam. Physician 62, 1064\u20131070, 1073 (2000).","journal-title":"Am. Fam. Physician"},{"key":"2337_CR4","doi-asserted-by":"publisher","unstructured":"Davies, B. M., Mowforth, O. D., Smith, E. K. & Kotter, M. R. Degenerative cervical myelopathy. BMJ https:\/\/doi.org\/10.1136\/bmj.k186 (2018).","DOI":"10.1136\/bmj.k186"},{"key":"2337_CR5","doi-asserted-by":"publisher","first-page":"S19","DOI":"10.1097\/BRS.0b013e3182a7f4de","volume":"38","author":"MG Fehlings","year":"2013","unstructured":"Fehlings, M. G. et al. Symptomatic progression of cervical myelopathy and the role of nonsurgical management: a consensus statement. Spine 38, S19\u2013S20 (2013).","journal-title":"Spine"},{"key":"2337_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.nec.2017.09.002","volume":"29","author":"JH Badhiwala","year":"2018","unstructured":"Badhiwala, J. H. & Wilson, J. R. The natural history of degenerative cervical myelopathy. Neurosurg. Clin. North Am. 29, 21\u201332 (2018).","journal-title":"Neurosurg. Clin. North Am."},{"key":"2337_CR7","doi-asserted-by":"publisher","first-page":"S21","DOI":"10.1097\/BRS.0b013e3182a7f2c3","volume":"38","author":"SK Karadimas","year":"2013","unstructured":"Karadimas, S. K., Erwin, W. M., Ely, C. G., Dettori, J. R. & Fehlings, M. G. Pathophysiology and natural history of cervical spondylotic myelopathy. Spine 38, S21\u2013S36 (2013).","journal-title":"Spine"},{"key":"2337_CR8","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1007\/s00586-022-07359-9","volume":"31","author":"E De Dios","year":"2022","unstructured":"De Dios, E., Laesser, M., Bj\u00f6rkman-Burtscher, I. M., Lindhagen, L. & MacDowall, A. Improvement rates, adverse events and predictors of clinical outcome following surgery for degenerative cervical myelopathy. Eur. Spine J. 31, 3433\u20133442 (2022).","journal-title":"Eur. Spine J."},{"key":"2337_CR9","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1097\/BRS.0000000000003305","volume":"45","author":"DH Pope","year":"2020","unstructured":"Pope, D. H., Mowforth, O. D., Davies, B. M. & Kotter, M. R. N. Diagnostic delays lead to greater disability in degenerative cervical myelopathy and represent a health inequality. Spine 45, 368\u2013377 (2020).","journal-title":"Spine"},{"key":"2337_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.797","volume":"7","author":"Z Kadanka","year":"2017","unstructured":"Kadanka, Z. et al. Predictors of symptomatic myelopathy in degenerative cervical spinal cord compression. Brain Behav. 7, e00797 (2017).","journal-title":"Brain Behav."},{"key":"2337_CR11","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s00586-008-0585-1","volume":"17","author":"J Bednarik","year":"2008","unstructured":"Bednarik, J. et al. Presymptomatic spondylotic cervical myelopathy: an updated predictive model. Eur. Spine J. 17, 421\u2013431 (2008).","journal-title":"Eur. Spine J."},{"key":"2337_CR12","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1177\/2192568220934496","volume":"11","author":"SS Smith","year":"2021","unstructured":"Smith, S. S., Stewart, M. E., Davies, B. M. & Kotter, M. R. N. The prevalence of asymptomatic and symptomatic spinal cord compression on magnetic resonance imaging: a systematic review and meta-analysis. Glob. Spine J. 11, 597\u2013607 (2021).","journal-title":"Glob. Spine J."},{"key":"2337_CR13","first-page":"235","volume":"66","author":"KK Sadasivan","year":"1993","unstructured":"Sadasivan, K. K., Reddy, R. P. & Albright, J. The natural history of cervical spondylotic myelopathy. Yale J. Biol. Med. 66, 235 (1993).","journal-title":"Yale J. Biol. Med."},{"key":"2337_CR14","doi-asserted-by":"publisher","first-page":"E1","DOI":"10.3171\/2013.3.FOCUS1374","volume":"35","author":"E Behrbalk","year":"2013","unstructured":"Behrbalk, E. et al. Delayed diagnosis of cervical spondylotic myelopathy by primary care physicians. Neurosurg. Focus 35, E1 (2013).","journal-title":"Neurosurg. Focus"},{"key":"2337_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-027000","volume":"9","author":"B Hilton","year":"2019","unstructured":"Hilton, B., Tempest-Mitchell, J., Davies, B. & Kotter, M. Route to diagnosis of degenerative cervical myelopathy in a UK healthcare system: a retrospective cohort study. BMJ Open 9, e027000 (2019).","journal-title":"BMJ Open"},{"key":"2337_CR16","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1097\/BOT.0000000000000485","volume":"30","author":"KE Radcliff","year":"2016","unstructured":"Radcliff, K. E. et al. High incidence of undiagnosed cervical myelopathy in patients with hip fracture compared with controls. J. Orthop. Trauma 30, 189\u2013193 (2016).","journal-title":"J. Orthop. Trauma"},{"key":"2337_CR17","doi-asserted-by":"publisher","first-page":"e060689","DOI":"10.1136\/bmjopen-2021-060689","volume":"12","author":"A Nouri","year":"2022","unstructured":"Nouri, A. et al. Can screening for degenerative cervical myelopathy (SCREEN-DCM) be effectively undertaken based on signs, symptoms and known risk factors? Rationale and research protocol for a prospective, multicentre, observational study. BMJ Open 12, e060689 (2022).","journal-title":"BMJ Open"},{"key":"2337_CR18","doi-asserted-by":"publisher","first-page":"e028455","DOI":"10.1136\/bmjopen-2018-028455","volume":"10","author":"M Waqar","year":"2020","unstructured":"Waqar, M., Wilcock, J., Garner, J., Davies, B. & Kotter, M. Quantitative analysis of medical students\u2019 and physicians\u2019 knowledge of degenerative cervical myelopathy. BMJ Open 10, e028455 (2020).","journal-title":"BMJ Open"},{"key":"2337_CR19","doi-asserted-by":"publisher","first-page":"e0207709","DOI":"10.1371\/journal.pone.0207709","volume":"13","author":"B Hilton","year":"2018","unstructured":"Hilton, B., Tempest-Mitchell, J., Davies, B. & Kotter, M. Assessment of degenerative cervical myelopathy differs between specialists and may influence time to diagnosis and clinical outcomes. PLoS ONE 13, e0207709 (2018).","journal-title":"PLoS ONE"},{"key":"2337_CR20","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","volume":"375","author":"Z Obermeyer","year":"2016","unstructured":"Obermeyer, Z. & Emanuel, E. J. Predicting the future \u2014 big data, machine learning, and clinical medicine. N. Engl. J. Med 375, 1216\u20131219 (2016).","journal-title":"N. Engl. J. Med"},{"key":"2337_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-17180-5","volume":"12","author":"I Ghanzouri","year":"2022","unstructured":"Ghanzouri, I. et al. Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Sci. Rep. 12, 13364 (2022).","journal-title":"Sci. Rep."},{"key":"2337_CR22","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooab011","volume":"4","author":"FR Tsui","year":"2021","unstructured":"Tsui, F. R. et al. Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts. JAMIA Open 4, ooab011 (2021).","journal-title":"JAMIA Open"},{"key":"2337_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-01100-0","volume":"10","author":"C Su","year":"2020","unstructured":"Su, C. et al. Machine learning for suicide risk prediction in children and adolescents with electronic health records. Transl. Psychiatry 10, 413 (2020).","journal-title":"Transl. Psychiatry"},{"key":"2337_CR24","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2012-002457","volume":"3","author":"B Farran","year":"2013","unstructured":"Farran, B., Channanath, A. M., Behbehani, K. & Thanaraj, T. A. Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait\u2014a cohort study. BMJ Open 3, e002457 (2013).","journal-title":"BMJ Open"},{"key":"2337_CR25","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3414\/ME13-02-0019","volume":"54","author":"G Jiang","year":"2015","unstructured":"Jiang, G. et al. Harmonization of detailed clinical models with clinical study data standards. Methods Inf. Med. 54, 65\u201374 (2015).","journal-title":"Methods Inf. Med."},{"key":"2337_CR26","doi-asserted-by":"publisher","first-page":"e33","DOI":"10.2196\/medinform.9455","volume":"6","author":"Y Huang","year":"2018","unstructured":"Huang, Y. et al. Privacy-preserving predictive modeling: harmonization of contextual embeddings from different sources. JMIR Med. Inf. 6, e33 (2018).","journal-title":"JMIR Med. Inf."},{"key":"2337_CR27","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259\u2013265 (2023).","journal-title":"Nature"},{"key":"2337_CR28","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s41746-025-01489-2","volume":"8","author":"C Alba","year":"2025","unstructured":"Alba, C., Xue, B., Abraham, J., Kannampallil, T. & Lu, C. The foundational capabilities of large language models in predicting postoperative risks using clinical notes. npj Digit. Med. 8, 95 (2025).","journal-title":"npj Digit. Med."},{"key":"2337_CR29","doi-asserted-by":"publisher","unstructured":"Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2108.07258 (2021).","DOI":"10.48550\/ARXIV.2108.07258"},{"key":"2337_CR30","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s41746-023-00879-8","volume":"6","author":"M Wornow","year":"2023","unstructured":"Wornow, M. et al. The shaky foundations of large language models and foundation models for electronic health records. npj Digit. Med. 6, 135 (2023).","journal-title":"npj Digit. Med."},{"key":"2337_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, S. et al. Diagnostic prediction for cervical spondylotic myelopathy based on multi-source data in electronic medical records. In Web Information Systems and Applications (eds Zhao, X., Yang, S., Wang, X. & Li, J.) 462\u2013470 (Springer International Publishing, 2022).","DOI":"10.1007\/978-3-031-20309-1_41"},{"key":"2337_CR32","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s41746-024-01166-w","volume":"7","author":"LL Guo","year":"2024","unstructured":"Guo, L. L. et al. A multi-center study on the adaptability of a shared foundation model for electronic health records. npj Digit. Med. 7, 171 (2024).","journal-title":"npj Digit. Med."},{"key":"2337_CR33","doi-asserted-by":"publisher","first-page":"e386","DOI":"10.1016\/S2589-7500(24)00050-5","volume":"6","author":"R Daniel","year":"2024","unstructured":"Daniel, R. et al. Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm. Lancet Digit. Health 6, e386\u2013e395 (2024).","journal-title":"Lancet Digit. Health"},{"key":"2337_CR34","doi-asserted-by":"crossref","unstructured":"Davies, B. M. et al. Improving awareness could transform outcomes in degenerative cervical myelopathy [AO spine RECODE-DCM research priority number 1]. Glob. Spine J. 12, 28S\u201338S (2022).","DOI":"10.1177\/21925682211050927"},{"key":"2337_CR35","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1093\/neuros\/nyy474","volume":"85","author":"L Tetreault","year":"2019","unstructured":"Tetreault, L. et al. Is preoperative duration of symptoms a significant predictor of functional outcomes in patients undergoing surgery for the treatment of degenerative cervical myelopathy?. Neurosurg 85, 642\u2013647 (2019).","journal-title":"Neurosurg"},{"key":"2337_CR36","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.wneu.2016.12.124","volume":"106","author":"T Oh","year":"2017","unstructured":"Oh, T. et al. Comparing quality of life in cervical spondylotic myelopathy with other chronic debilitating diseases using the short form survey 36-health survey. World Neurosurg. 106, 699\u2013706 (2017).","journal-title":"World Neurosurg."},{"key":"2337_CR37","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/s41592-024-02523-z","volume":"22","author":"H Dalla-Torre","year":"2025","unstructured":"Dalla-Torre, H. et al. Nucleotide transformer: building and evaluating robust foundation models for human genomics. Nat. Methods 22, 287\u2013297 (2025).","journal-title":"Nat. Methods"},{"key":"2337_CR38","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/s42256-024-00807-9","volume":"6","author":"S Pai","year":"2024","unstructured":"Pai, S. et al. Foundation model for cancer imaging biomarkers. Nat. Mach. Intell. 6, 354\u2013367 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"2337_CR39","doi-asserted-by":"publisher","unstructured":"Azad, B. et al. Foundational models in medical imaging: a comprehensive survey and future vision. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2310.18689 (2023).","DOI":"10.48550\/ARXIV.2310.18689"},{"key":"2337_CR40","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019).","journal-title":"Nat. Med."},{"key":"2337_CR41","first-page":"1","volume":"21","author":"SJ Pan","year":"2020","unstructured":"Pan, S. J. Transfer learning. Learning 21, 1\u20132 (2020).","journal-title":"Learning"},{"key":"2337_CR42","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J. H. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Vol. 2 (Springer, 2009).","DOI":"10.1007\/978-0-387-84858-7"},{"key":"2337_CR43","unstructured":"Hoffmann, J. et al. Training compute-optimal large language models. In Proc. 36th International Conference on Neural Information Processing Systems 30016\u201330030 (Curran Associates Inc., Red Hook, NY, USA, 2022)."},{"key":"2337_CR44","doi-asserted-by":"publisher","unstructured":"Kaplan, J. et al. Scaling laws for neural language models. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2001.08361 (2020).","DOI":"10.48550\/ARXIV.2001.08361"},{"key":"2337_CR45","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798\u20131828 (2013).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2337_CR46","doi-asserted-by":"publisher","first-page":"103621","DOI":"10.1016\/j.jbi.2020.103621","volume":"113","author":"SR Pfohl","year":"2021","unstructured":"Pfohl, S. R., Foryciarz, A. & Shah, N. H. An empirical characterization of fair machine learning for clinical risk prediction. J. Biomed. Inform. 113, 103621 (2021).","journal-title":"J. Biomed. Inform."},{"key":"2337_CR47","doi-asserted-by":"publisher","unstructured":"Gu, Y., Dong, L., Wei, F. & Huang, M. MiniLLM: knowledge distillation of large language models. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2306.08543 (2023).","DOI":"10.48550\/ARXIV.2306.08543"},{"key":"2337_CR48","doi-asserted-by":"publisher","unstructured":"Stanford Center for Population Health Sciences. MarketScan Commercial Database. 7891975343681 bytes, 9 tables, 240 variables, 32183633541 records, 0 files Redivis https:\/\/doi.org\/10.57761\/N5V8-0V21 (2025).","DOI":"10.57761\/N5V8-0V21"},{"key":"2337_CR49","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/S1474-4422(19)30168-1","volume":"18","author":"The Lancet Neurology.","year":"2019","unstructured":"The Lancet Neurology. A focus on patient outcomes in cervical myelopathy. Lancet Neurol. 18, 615 (2019).","journal-title":"Lancet Neurol."},{"key":"2337_CR50","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/S1474-4422(25)00074-2","volume":"24","author":"JJ Wardropper","year":"2025","unstructured":"Wardropper, J. J., Demetriades, A. K. & Anderson, D. B. Raising awareness of degenerative cervical myelopathy. Lancet Neurol. 24, 286\u2013287 (2025).","journal-title":"Lancet Neurol."},{"key":"2337_CR51","unstructured":"Arnrich, B. et al. Medical event data standard (MEDS): facilitating machine learning for health. In ICLR 2024 Workshop on Learning from Time Series For Health 03\u201308 (ICLR, 2024)."},{"key":"2337_CR52","doi-asserted-by":"publisher","unstructured":"Wornow, M., Thapa, R., Steinberg, E., Fries, J. A. & Shah, N. H. EHRSHOT: an EHR benchmark for few-shot evaluation of foundation models. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2307.02028 (2023).","DOI":"10.48550\/ARXIV.2307.02028"},{"key":"2337_CR53","doi-asserted-by":"publisher","unstructured":"Odgaard, M. et al. CORE-BEHRT: a carefully optimized and rigorously evaluated BEHRT. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2404.15201 (2024).","DOI":"10.48550\/ARXIV.2404.15201"},{"key":"2337_CR54","doi-asserted-by":"publisher","unstructured":"Pang, C. et al. CEHR-BERT: incorporating temporal information from structured EHR data to improve prediction tasks. https:\/\/doi.org\/10.48550\/ARXIV.2111.08585. (2021).","DOI":"10.48550\/ARXIV.2111.08585"},{"key":"2337_CR55","unstructured":"Gu, A. & Dao, T. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In First Conference on Language Modeling (2024)."},{"key":"2337_CR56","doi-asserted-by":"publisher","unstructured":"Steinberg, E., Fries, J., Xu, Y. & Shah, N. MOTOR: a time-to-event foundation model for structured medical records. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2301.03150 (2023).","DOI":"10.48550\/ARXIV.2301.03150"},{"key":"2337_CR57","doi-asserted-by":"publisher","unstructured":"Ba, J. L., Kiros, J. R. & Hinton, G. E. Layer normalization. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.1607.06450 (2016).","DOI":"10.48550\/ARXIV.1607.06450"},{"key":"2337_CR58","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digit. Med. 1, 18 (2018).","journal-title":"npj Digit. Med."},{"key":"2337_CR59","doi-asserted-by":"publisher","unstructured":"Kumar, A., Raghunathan, A., Jones, R., Ma, T. & Liang, P. Fine-tuning can distort pretrained features and underperform out-of-distribution. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2202.10054 (2022).","DOI":"10.48550\/ARXIV.2202.10054"},{"key":"2337_CR60","doi-asserted-by":"publisher","unstructured":"Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L. & Lewis, M. Generalization through memorization: nearest neighbor language models. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.1911.00172 (2019).","DOI":"10.48550\/ARXIV.1911.00172"},{"key":"2337_CR61","doi-asserted-by":"crossref","unstructured":"Prechelt, L. Early stopping \u2014 but when? In Neural Networks: Tricks of the Trade (eds Orr, G., Montavon, G. & M\u00fcller, K. -R.) Ch. 2, 53\u221268 (Springer, 2002).","DOI":"10.1007\/978-3-642-35289-8_5"},{"key":"2337_CR62","doi-asserted-by":"crossref","unstructured":"Britto, C. F. Lung cancer prediction using variational autoencoders and early stopping for neural network clustering and optimal tuning. In Proc. 5th International Conference on Data Science, Machine Learning and Applications (eds Kumar, A., Gunjan, V. K., Senatore, S. & Hu, Y.-C.) 1273 650\u2013658 (Springer Nature, 2025).","DOI":"10.1007\/978-981-97-8031-0_69"},{"key":"2337_CR63","doi-asserted-by":"publisher","unstructured":"Liu, H., Li, Z., Hall, D., Liang, P. & Ma, T. Sophia: a scalable stochastic second-order optimizer for language model pre-training. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2305.14342 (2023).","DOI":"10.48550\/ARXIV.2305.14342"},{"key":"2337_CR64","doi-asserted-by":"publisher","first-page":"4101","DOI":"10.21105\/joss.04101","volume":"7","author":"N Detlefsen","year":"2022","unstructured":"Detlefsen, N. et al. TorchMetrics - measuring reproducibility in PyTorch. J. Open Source Softw. 7, 4101 (2022).","journal-title":"J. Open Source Softw."},{"key":"2337_CR65","doi-asserted-by":"publisher","unstructured":"Kokhlikyan, N. et al. Captum: a unified and generic model interpretability library for PyTorch. Preprint at https:\/\/doi.org\/10.48550\/ARXIV.2009.07896 (2020).","DOI":"10.48550\/ARXIV.2009.07896"},{"key":"2337_CR66","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10, e0118432 (2015).","journal-title":"PLoS ONE"},{"key":"2337_CR67","doi-asserted-by":"crossref","unstructured":"Davis, J. & Goadrich, M. The relationship between Precision-Recall and ROC curves. In Proc. 23rd International Conference on Machine learning - ICML \u201906 233\u2013240 (ACM Press, 2006).","DOI":"10.1145\/1143844.1143874"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02337-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02337-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02337-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:04:03Z","timestamp":1770901443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02337-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2337"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02337-7","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,20]]},"assertion":[{"value":"15 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 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":"153"}}