{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007332278,0.0001332402,0.0002503107,0.0001765958,0.0004346945,0.0001021124,0.0001978743,0.00003660688,0.0003598671],"category_scores_gemma":[0.003561273,0.0001152215,0.00003161548,0.0001408407,0.00006406289,0.0003479517,0.00002015705,0.0006247465,0.000003275503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003051037,"about_ca_system_score_gemma":0.001163713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003632584,"about_ca_topic_score_gemma":0.002855439,"domain_scores_codex":[0.9984319,0.0001595823,0.0006972646,0.00008674082,0.0003788697,0.000245595],"domain_scores_gemma":[0.9971325,0.0009715008,0.0007497224,0.0001375659,0.0006435303,0.0003651228],"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":[0.0001110077,0.00003000633,0.002158453,0.00006998585,0.0001431737,0.0001020425,0.007007693,0.06684586,0.00002195705,0.8263506,0.01967512,0.07748404],"study_design_scores_gemma":[0.0002197084,0.0001813352,0.0001176697,0.00004105104,0.00006738958,0.00002599787,0.0007410079,0.5403903,0.000004080997,0.4578322,0.0002971351,0.00008213936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008740012,0.00002235894,0.979781,0.0000227189,0.0002398176,0.0001012038,0.007629897,0.000009617983,0.003453329],"genre_scores_gemma":[0.1990758,0.000005438436,0.8006939,0.0000766524,0.00006228784,0.000006135209,0.00003666014,0.00001474231,0.00002834684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4735444,"threshold_uncertainty_score":0.5491405,"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."}}