{"id":"W3048298707","doi":"10.1503/cmaj.191297","title":"Use of the Population Grouping Methodology of the Canadian Institute for Health Information to predict high-cost health system users in Ontario","year":2020,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Medical Association","funders":"","keywords":"Population; Health care; Total cost; Population health; Actuarial science; Statistic; Medicine; Environmental health; Statistics; Demography; Computer science; Business; Economics; Accounting; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.005646429,0.0008631784,0.0008387483,0.003216048,0.001523551,0.001377417,0.002313171,0.0006137289,0.0026145],"category_scores_gemma":[0.0171818,0.0003115542,0.001587347,0.005122001,0.0005544114,0.0006152379,0.001465861,0.001051275,0.0002626407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02179517,"about_ca_system_score_gemma":0.03608373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9661773,"about_ca_topic_score_gemma":0.954883,"domain_scores_codex":[0.9966181,0.0008288672,0.0002558342,0.0005721229,0.001305556,0.000419567],"domain_scores_gemma":[0.9947779,0.0009456523,0.001018899,0.0004607763,0.002493846,0.0003028277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00019856,0.00009300377,0.9060813,0.000220223,0.0004401249,0.0001194276,0.0007743614,0.03728758,0.0002949164,0.003207585,0.01505741,0.03622563],"study_design_scores_gemma":[0.0001046627,0.00011098,0.8516992,0.0001473296,0.0002263722,0.00008611487,0.0007529501,0.1349028,0.0003811112,0.002249376,0.009267086,0.00007197164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7912385,0.001774531,0.110903,0.004011471,0.0003282155,0.005646554,0.0653472,0.001038044,0.01971235],"genre_scores_gemma":[0.9489311,0.0003879758,0.03299183,0.0002288792,0.00004400373,0.001347821,0.01409462,0.00004157505,0.001932197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03382272,"threshold_uncertainty_score":0.1581358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09464059679719876,"score_gpt":0.3401908566712858,"score_spread":0.245550259874087,"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."}}