{"id":"W4220848513","doi":"10.1136/bmjopen-2021-051403","title":"Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study","year":2022,"lang":"en","type":"article","venue":"BMJ Open","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Vector Institute; Trillium Health Centre; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Ambulatory; Cohort; Cohort study; Health care; Population; Epidemiology; Public health; Ambulatory care; Population health; Gerontology; Environmental health; Internal medicine; Nursing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.01188732,0.0008519738,0.0005794117,0.0007396071,0.0007013282,0.0008417624,0.001293552,0.0005911235,0.0008314308],"category_scores_gemma":[0.01465233,0.0004340004,0.001299892,0.0006840044,0.0006758738,0.0004170883,0.001015718,0.001090288,0.0003992041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001864166,"about_ca_system_score_gemma":0.00444581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1274401,"about_ca_topic_score_gemma":0.1037152,"domain_scores_codex":[0.9983109,0.0009102444,0.00007587421,0.0002842347,0.0002856104,0.0001330008],"domain_scores_gemma":[0.9919475,0.003055647,0.0007301532,0.002075172,0.001754761,0.0004368403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001104084,0.0007962923,0.9750969,0.00002616236,0.0004313306,0.0001421158,0.0001470278,0.008801078,0.0004051481,0.00010546,0.001188942,0.01175547],"study_design_scores_gemma":[0.0006600429,0.001448939,0.8161145,0.00006465447,0.0004892153,0.0003308609,0.0003199991,0.1773109,0.001231419,0.0003362748,0.001634388,0.00005894261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934329,0.00009289149,0.004452305,0.00008193948,0.00001225116,0.0002737533,0.00132426,0.00004065333,0.0002890923],"genre_scores_gemma":[0.9899834,0.00008250222,0.005660403,0.0000500745,0.00001087318,0.0001778133,0.003611099,0.0000131249,0.0004107344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1274401,"threshold_uncertainty_score":0.2533966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08052681019010886,"score_gpt":0.4838761934418271,"score_spread":0.4033493832517182,"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."}}