{"id":"W4224066525","doi":"10.1002/cjs.11701","title":"Integrating information from existing risk prediction models with no model details","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Human Genome Research Institute; School of Public Health, University of Michigan; National Institutes of Health; National Science Foundation","keywords":"Computer science; Risk model; Model risk; Data science; Data mining; Risk analysis (engineering); Risk management; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0116935,0.001779319,0.002128558,0.001660214,0.000481133,0.002736479,0.003746593,0.00162774,0.002441898],"category_scores_gemma":[0.0551611,0.001518812,0.001750603,0.00213778,0.001373021,0.005686786,0.002583106,0.003147002,0.0007468072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141584,"about_ca_system_score_gemma":0.002234998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005892674,"about_ca_topic_score_gemma":0.006147734,"domain_scores_codex":[0.9968281,0.001405437,0.0002359587,0.0006609074,0.0006997696,0.0001697349],"domain_scores_gemma":[0.9719493,0.0204436,0.001821674,0.004266443,0.001194885,0.0003240313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002607462,0.0002396763,0.01727636,0.0002186364,0.0003517474,0.0003346278,0.0002049595,0.824073,0.001246482,0.06316848,0.0009642969,0.09166096],"study_design_scores_gemma":[0.00002205986,0.00006692431,0.001223302,0.00002537034,0.00006727593,0.00004105434,0.00001084469,0.9557701,0.0006846059,0.04144228,0.0006104919,0.00003567445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02783734,0.0002494059,0.9696254,0.0004667647,0.00002452302,0.00004361042,0.0003144415,0.0005269622,0.0009116725],"genre_scores_gemma":[0.6661037,0.0007805217,0.3282168,0.0003863773,0.000173401,0.0002486617,0.001691365,0.0001959566,0.002203219],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0116935,"threshold_uncertainty_score":0.06184185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0946525169348207,"score_gpt":0.2867892235709923,"score_spread":0.1921367066361717,"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."}}