{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T15:59:24Z","timestamp":1791215964438,"version":"4.1.0"},"reference-count":44,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3]]},"abstract":"\n Objectives:<\/jats:title>\n To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF.<\/jats:p>\n <\/jats:sec>\n \n Methods:<\/jats:title>\n Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs: (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool.<\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.02, \u22120.06, and 0.106, respectively. This prediction model was deployed as an open-access online prediction tool.<\/jats:p>\n <\/jats:sec>\n \n Conclusion:<\/jats:title>\n A ML-based prediction model accurately predicted the probability of a (occult) PMF in patients with a TSF based on patient- and fracture-specific characteristics. This prediction model can guide surgeons in their diagnostic workup and preoperative planning. Further research is required to externally validate the model before implementation in clinical practice.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence:<\/jats:title>\n Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.<\/jats:p>\n <\/jats:sec>","DOI":"10.1097\/bot.0000000000001663","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T10:16:39Z","timestamp":1571307399000},"page":"131-138","source":"Crossref","is-referenced-by-count":36,"title":["A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide \u201cMalleolus First\u201d Fixation"],"prefix":"10.1097","volume":"34","author":[{"given":"Laurent A. M.","family":"Hendrickx","sequence":"first","affiliation":[{"name":"Department of Orthopaedic and Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia;"},{"name":"Department of Orthopaedic Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam, The Netherlands;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Garret L.","family":"Sobol","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Rutgers New Jersey Medical School, Newark, NJ;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W. 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