{"id":"W4410429608","doi":"10.1002/sim.70077","title":"A Personalized Predictive Model That Jointly Optimizes Discrimination and Calibration","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Calibration; Computer science; Flexibility (engineering); Function (biology); Measure (data warehouse); Population; Field (mathematics); A-weighting; Machine learning; Artificial intelligence; Similarity (geometry); Statistics; Econometrics; Data mining; Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005534981,0.001117827,0.002419668,0.0009235752,0.0006245631,0.001880346,0.002631201,0.002839624,0.0017326],"category_scores_gemma":[0.01892563,0.0008007815,0.001361344,0.001250752,0.001105712,0.002825127,0.002157883,0.003448237,0.0006670392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001492229,"about_ca_system_score_gemma":0.001945988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00468949,"about_ca_topic_score_gemma":0.003230233,"domain_scores_codex":[0.9978969,0.0009184008,0.00009298901,0.0006534918,0.0002574445,0.0001807126],"domain_scores_gemma":[0.9941736,0.004031354,0.0005652959,0.0005303403,0.000499455,0.0002000196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001533962,0.00008189294,0.004345472,0.00004754122,0.00008696972,0.00009595598,0.0001049515,0.9262031,0.001003277,0.01427911,0.002032608,0.05156574],"study_design_scores_gemma":[0.00001853317,0.00005093582,0.0004814761,0.00001450016,0.00002756853,0.0000623325,0.0000147748,0.9836555,0.0004111836,0.01470642,0.0005405728,0.00001624261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04538825,0.0002255725,0.9508155,0.001250359,0.00004303862,0.00009792523,0.0002567691,0.0005182532,0.001404355],"genre_scores_gemma":[0.7285581,0.0002953968,0.2634145,0.001067072,0.0001660361,0.000470929,0.0008932488,0.0002031021,0.00493165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005534981,"threshold_uncertainty_score":0.02927208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02859648161822631,"score_gpt":0.3426640047874073,"score_spread":0.314067523169181,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}