{"id":"W3200669805","doi":"10.23889/ijpds.v6i1.1650","title":"Machine learning for identification of frailty in Canadian primary care practices","year":2021,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Frailty in Older Adults","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Manitoba; McMaster University; Dalhousie University; Manitoba Health; University of British Columbia; University of Calgary","funders":"Canadian Frailty Network; Michael Smith Health Research BC","keywords":"Machine learning; Context (archaeology); Receiver operating characteristic; Medicine; Artificial intelligence; Medical record; Primary care; Oversampling; Computer science; Family medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001100021,0.00006135654,0.0001144668,0.0004042746,0.0001590657,0.000130759,0.00055744,0.00003484581,0.00002576838],"category_scores_gemma":[0.007419151,0.00006157855,0.00003673582,0.0002815588,0.00005366641,0.001468856,0.00008531443,0.0001550186,0.000001529854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005740341,"about_ca_system_score_gemma":0.001393157,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01848004,"about_ca_topic_score_gemma":0.07563113,"domain_scores_codex":[0.9984795,0.00002317755,0.0004577098,0.0002679221,0.0005944411,0.000177216],"domain_scores_gemma":[0.9973482,0.00012929,0.0005652937,0.0002527205,0.001571164,0.0001333378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002209086,0.0001123488,0.8949057,0.0001841609,0.00005608962,0.00003470623,0.0007907731,0.002959913,0.0235317,0.001844465,0.0004880973,0.07487111],"study_design_scores_gemma":[0.001619055,0.00007503126,0.8603371,0.0002638726,0.00003973259,0.0003181343,0.0004417036,0.08955932,0.002661053,0.0004928173,0.04407471,0.0001174165],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509138,0.001479676,0.01955005,0.01688498,0.00592902,0.001276801,0.00271612,0.00003202614,0.001217544],"genre_scores_gemma":[0.9858609,0.00006802517,0.008637867,0.0001573501,0.0001790653,0.000007418922,0.00471174,0.000007907283,0.0003696714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08659941,"threshold_uncertainty_score":0.988056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09551632604470203,"score_gpt":0.4260697510684341,"score_spread":0.3305534250237321,"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."}}