{"id":"W2889680783","doi":"10.23889/ijpds.v3i4.762","title":"Predicting health care utilization using CIHI's Population Grouping Methodology","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Health care; Population; Population health; Business; Medicine; Environmental health; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004489529,0.0009805318,0.0007721824,0.004523523,0.0006580831,0.001165163,0.001020649,0.0008524326,0.004788445],"category_scores_gemma":[0.01107222,0.000291204,0.001434678,0.004512467,0.000337179,0.0005432208,0.001173676,0.0009237675,0.0008307592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645773,"about_ca_system_score_gemma":0.002191632,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05771378,"about_ca_topic_score_gemma":0.02348169,"domain_scores_codex":[0.9975914,0.001325157,0.0001473886,0.0003117712,0.0004007939,0.0002235685],"domain_scores_gemma":[0.9947042,0.002859815,0.0005975998,0.0006663276,0.0009168306,0.0002552487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002809087,0.0005215318,0.7071773,0.0001892974,0.0004158773,0.0001679369,0.0005807162,0.1438542,0.0004673657,0.005532365,0.01782341,0.1229891],"study_design_scores_gemma":[0.000069713,0.0005326796,0.2590593,0.0001121775,0.0001457856,0.0001590767,0.00106997,0.7238376,0.001102695,0.005454087,0.008368295,0.00008861472],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7698576,0.0006803529,0.1765426,0.0027325,0.0002458472,0.00237411,0.02736213,0.00214459,0.01806025],"genre_scores_gemma":[0.8761476,0.0002722312,0.1092238,0.00021085,0.0001023492,0.001288686,0.01086663,0.00004085031,0.00184693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9422863,"threshold_uncertainty_score":0.1147557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4771458056351071,"score_gpt":0.5846272929485187,"score_spread":0.1074814873134116,"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."}}