{"id":"W2936048997","doi":"10.6000/1929-6029.2019.08.01","title":"Bayesian Model Averaging for Selection of a Risk Prediction Model for Death within Thirty Days of Discharge: The SILVER-AMI Study","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Akaike information criterion; Bayesian information criterion; Statistics; Model selection; Observational study; Bayes' theorem; Selection (genetic algorithm); Statistic; Context (archaeology); Bayesian probability; Posterior probability; Mathematics; Econometrics; Medicine; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01149172,0.0001155131,0.0003901236,0.0003312016,0.00007344977,0.00003465854,0.0006652505,0.0001005418,0.0000806883],"category_scores_gemma":[0.03608852,0.00007729302,0.00008663769,0.0001755074,0.0001564551,0.0001090866,0.00009937894,0.0008340285,3.871801e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001601275,"about_ca_system_score_gemma":0.0007007651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005726753,"about_ca_topic_score_gemma":0.00007980943,"domain_scores_codex":[0.9951245,0.0004383659,0.001277128,0.0001884635,0.002723494,0.0002480608],"domain_scores_gemma":[0.9824165,0.01398174,0.0006417483,0.0001485238,0.002693724,0.0001178071],"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":[0.001948918,0.001551256,0.02746543,0.0003596978,0.0003833265,0.0000101818,0.006193004,0.01273024,0.0004139232,0.91597,0.001299997,0.03167406],"study_design_scores_gemma":[0.0009894653,0.0003586097,0.0007983058,0.0001779755,0.00002640064,0.000005341852,0.0003710049,0.5399535,0.0001042464,0.4571778,0.000002360583,0.00003492057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04919872,0.00000998552,0.9485536,0.0001851373,0.0002676649,0.0006964364,0.001013069,0.000003244441,0.00007216998],"genre_scores_gemma":[0.5809309,0.00003566546,0.418874,0.00001001459,0.0000773268,0.00002553015,0.000004771083,0.00001254343,0.00002924314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5317322,"threshold_uncertainty_score":0.9720309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1178404149115008,"score_gpt":0.4817388968654298,"score_spread":0.363898481953929,"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."}}