{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T22:45:10Z","timestamp":1776379510892,"version":"3.51.2"},"reference-count":23,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01CA230442"],"award-info":[{"award-number":["R01CA230442"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["eBioMedicine"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.ebiom.2025.106118","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:51:09Z","timestamp":1767869469000},"page":"106118","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Real world deployment of a pancreatic cancer risk model: impact of refitting, imputation, and computational burden"],"prefix":"10.1016","volume":"124","author":[{"given":"Wansu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-9248","authenticated-orcid":false,"given":"Tiffany Q.","family":"Luong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fagen","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bechien U.","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ebiom.2025.106118_bib2","doi-asserted-by":"crossref","DOI":"10.2196\/51240","article-title":"Identifying symptoms prior to pancreatic ductal adenocarcinoma diagnosis in real world care settings: natural language processing approach","volume":"3","author":"Xie","year":"2024","journal-title":"JMIR AI"},{"issue":"7","key":"10.1016\/j.ebiom.2025.106118_bib3","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.pan.2025.05.005","article-title":"Identifying signs and symptoms of pancreatic cancer: a population-based study using electronic health records and natural language processing","volume":"25","author":"Chen","year":"2025","journal-title":"Pancreatology"},{"issue":"8","key":"10.1016\/j.ebiom.2025.106118_bib4","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.14309\/ajg.0000000000002870","article-title":"Machine learning models for pancreatic cancer risk prediction using electronic health record data-a systematic review and assessment","volume":"119","author":"Mishra","year":"2024","journal-title":"Am J Gastroenterol"},{"issue":"1","key":"10.1016\/j.ebiom.2025.106118_bib5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.14309\/ajg.0000000000002050","article-title":"Derivation and external validation of machine learning-based model for detection of pancreatic cancer","volume":"118","author":"Chen","year":"2023","journal-title":"Am J Gastroenterol"},{"issue":"4","key":"10.1016\/j.ebiom.2025.106118_bib6","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.pan.2023.04.009","article-title":"Machine learning versus regression for prediction of sporadic pancreatic cancer","volume":"23","author":"Chen","year":"2023","journal-title":"Pancreatology"},{"key":"10.1016\/j.ebiom.2025.106118_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2023.104888","article-title":"A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data","volume":"98","author":"Jia","year":"2023","journal-title":"eBioMedicine"},{"issue":"1","key":"10.1016\/j.ebiom.2025.106118_bib8","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41698-024-00553-6","article-title":"All models are wrong and yours are useless: making clinical prediction models impactful for patients","volume":"8","author":"Markowetz","year":"2024","journal-title":"NPJ Precis Oncol"},{"issue":"5","key":"10.1016\/j.ebiom.2025.106118_bib9","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2023-073174","article-title":"What proportion of clinical prediction models make it to clinical practice? Protocol for a two-track follow-up study of prediction model development publications","volume":"13","author":"Arshi","year":"2023","journal-title":"BMJ Open"},{"key":"10.1016\/j.ebiom.2025.106118_bib10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jclinepi.2021.01.003","article-title":"Real-time imputation of missing predictor values improved the application of prediction models in daily practice","volume":"134","author":"Nijman","year":"2021","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"10.1016\/j.ebiom.2025.106118_bib11","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1093\/ehjdh\/ztaa016","article-title":"Real-time imputation of missing predictor values in clinical practice","volume":"2","author":"Nijman","year":"2020","journal-title":"Eur Heart J Digit Health"},{"issue":"25","key":"10.1016\/j.ebiom.2025.106118_bib12","doi-asserted-by":"crossref","first-page":"3591","DOI":"10.1002\/sim.8682","article-title":"Handling missing predictor values when validating and applying a prediction model to new patients","volume":"39","author":"Hoogland","year":"2020","journal-title":"Stat Med"},{"key":"10.1016\/j.ebiom.2025.106118_bib13","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jclinepi.2020.03.028","article-title":"Missing data should be handled differently for prediction than for description or causal explanation","volume":"125","author":"Sperrin","year":"2020","journal-title":"J Clin Epidemiol"},{"issue":"2","key":"10.1016\/j.ebiom.2025.106118_bib14","doi-asserted-by":"crossref","first-page":"87","DOI":"10.7812\/TPP\/22.172","article-title":"Comparing kaiser permanente members to the general population: implications for generalizability of research","volume":"27","author":"Davis","year":"2023","journal-title":"Perm J"},{"issue":"2","key":"10.1016\/j.ebiom.2025.106118_bib15","article-title":"Research data warehouse: using electronic health records to conduct population-based observational studies","volume":"6","author":"Chen","year":"2023","journal-title":"JAMIA Open"},{"issue":"10","key":"10.1016\/j.ebiom.2025.106118_bib16","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1002\/sim.6428","article-title":"Tests of calibration and goodness-of-fit in the survival setting","volume":"34","author":"Demler","year":"2015","journal-title":"Stat Med"},{"key":"10.1016\/j.ebiom.2025.106118_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103611","article-title":"Detection of calibration drift in clinical prediction models to inform model updating","volume":"112","author":"Davis","year":"2020","journal-title":"J Biomed Inform"},{"issue":"1","key":"10.1016\/j.ebiom.2025.106118_bib18","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1186\/s12874-022-01801-8","article-title":"Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review","volume":"22","author":"Binuya","year":"2022","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"10.1016\/j.ebiom.2025.106118_bib19","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1055\/s-0041-1735184","article-title":"Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine","volume":"12","author":"Guo","year":"2021","journal-title":"Appl Clin Inform"},{"key":"10.1016\/j.ebiom.2025.106118_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2022.104930","article-title":"Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction","volume":"173","author":"Rahmani","year":"2023","journal-title":"Int J Med Inform"},{"issue":"1150","key":"10.1016\/j.ebiom.2025.106118_bib21","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20220878","article-title":"Data drift in medical machine learning: implications and potential remedies","volume":"96","author":"Sahiner","year":"2023","journal-title":"Br J Radiol"},{"issue":"2","key":"10.1016\/j.ebiom.2025.106118_bib22","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s10676-025-09829-2","article-title":"A moving target in AI-assisted decision-making: dataset shift, model updating, and the problem of update opacity","volume":"27","author":"Hatherley","year":"2025","journal-title":"Ethics Inf Technol"},{"key":"10.1016\/j.ebiom.2025.106118_bib23","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.3389\/fdata.2021.693674","article-title":"A benchmark for data imputation methods","volume":"4","author":"J\u00e4ger","year":"2021","journal-title":"Front Big Data"},{"issue":"6","key":"10.1016\/j.ebiom.2025.106118_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.gastha.2025.100657","article-title":"Tailored strategies are needed to address racial and ethnic disparities in pancreatic cancer risk prediction","volume":"4","author":"Chen","year":"2025","journal-title":"Gastro Hep Adv"}],"container-title":["eBioMedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396425005687?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352396425005687?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T02:42:32Z","timestamp":1775097752000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352396425005687"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":23,"alternative-id":["S2352396425005687"],"URL":"https:\/\/doi.org\/10.1016\/j.ebiom.2025.106118","relation":{},"ISSN":["2352-3964"],"issn-type":[{"value":"2352-3964","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Real world deployment of a pancreatic cancer risk model: impact of refitting, imputation, and computational burden","name":"articletitle","label":"Article Title"},{"value":"eBioMedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ebiom.2025.106118","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"106118"}}