{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T06:33:51Z","timestamp":1793169231590,"version":"4.5.0"},"reference-count":25,"publisher":"Walter de Gruyter GmbH","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_dx-2025-0152_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Clinical prediction requires formalizing uncertainty into a statistical model. However, persistent confusion between prediction and inference, and between traditional (stepwise) and modern (penalized) development strategies, leads to unstable, poorly calibrated, and overfit models. A structured statistical framework is essential.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2025-0152_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This article is a structured, didactic tutorial that explains the core concepts of clinical prediction models. It covers the definition of a prediction model, the fundamental strategies for its construction, and the essential framework for its evaluation, illustrated through an applied example using real-world clinical\u00a0data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2025-0152_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The tutorial illustrates model development using the GUSTO-I dataset (\n                      <jats:italic>N<\/jats:italic>\n                      = 40,830). Penalized methods (LASSO and Elastic Net) successfully identified clinical signals while eliminating engineered noise variables. The LASSO model (\u03bb\n                      <jats:sub>1se<\/jats:sub>\n                      ) achieved excellent discrimination (AUC 0.818; 95\u202f% CI:\u00a00.803\u20130.832) and overall accuracy (Brier score 0.058). Calibration analysis revealed a slope of 1.28 and intercept of\u00a00.63, identifying conservative bias and systematic risk underestimation inherent to \u03bb\n                      <jats:sub>1se<\/jats:sub>\n                      selection. Decision curve analysis confirmed significant clinical utility across relevant probability thresholds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_dx-2025-0152_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This guide equips clinicians with a rigorous methodological framework for the critical appraisal and interpretation of modern clinical prediction models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/dx-2025-0152","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T16:06:51Z","timestamp":1772294811000},"source":"Crossref","is-referenced-by-count":2,"title":["Clinical prediction models: from foundational concepts to practical application"],"prefix":"10.1515","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0943-6367","authenticated-orcid":false,"given":"Javier","family":"Arredondo Montero","sequence":"first","affiliation":[{"name":"Pediatric Surgery Department , Complejo Asistencial Universitario de Le\u00f3n , Le\u00f3n , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"2026102805554826264_j_dx-2025-0152_ref_001","doi-asserted-by":"crossref","unstructured":"Steyerberg, EW. 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