{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004468351,0.0001046802,0.0001931411,0.0002146624,0.00008368544,0.00003102345,0.0001888583,0.00004880942,0.00001105036],"category_scores_gemma":[0.0007812082,0.00008839751,0.000007355807,0.0002298275,0.0001478198,0.0001642379,0.00009949382,0.0002246371,3.76313e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007432626,"about_ca_system_score_gemma":0.0001038359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002587264,"about_ca_topic_score_gemma":0.0001023327,"domain_scores_codex":[0.9989207,0.000140917,0.0002257266,0.0002894355,0.0002727366,0.0001505178],"domain_scores_gemma":[0.9991805,0.0004095655,0.00007727003,0.0001973434,0.00008217733,0.00005317315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003573118,0.00002170488,0.006581024,0.0002346215,0.00001072173,0.00001379524,0.009434178,0.00921044,0.00003773547,0.9517881,0.004014145,0.01861775],"study_design_scores_gemma":[0.0006524437,0.00008345189,0.008266279,0.0001898559,0.000009862539,0.000002183743,0.0002740762,0.8915665,0.000009386067,0.09884671,0.00003880968,0.00006051116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009685651,0.0003270897,0.9873819,0.009127222,0.0002141949,0.0002225504,0.00002209211,0.0000566546,0.001679722],"genre_scores_gemma":[0.5760469,0.0001739243,0.421783,0.00104567,0.00003527562,0.00003748928,0.00005007155,0.000007134295,0.0008205303],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.882356,"threshold_uncertainty_score":0.3604746,"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."}}