{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006406649,0.0007027119,0.0006752325,0.003289193,0.001834126,0.001324773,0.001772076,0.0006871216,0.002258136],"category_scores_gemma":[0.03829401,0.0003500117,0.001256161,0.003164422,0.0005106262,0.000593354,0.001108032,0.00122126,0.0003457415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03018992,"about_ca_system_score_gemma":0.02959082,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9685681,"about_ca_topic_score_gemma":0.958391,"domain_scores_codex":[0.996837,0.0008091009,0.0002227412,0.0004779756,0.001213904,0.0004393017],"domain_scores_gemma":[0.9892287,0.003796052,0.001002757,0.0004251668,0.004851392,0.000695957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002668551,0.000144448,0.9214319,0.0003644479,0.0003111324,0.0001723559,0.0006128557,0.01638373,0.0001470602,0.0008160549,0.008312955,0.05103623],"study_design_scores_gemma":[0.00007768154,0.0001443998,0.8236528,0.0004408998,0.0001785036,0.0002264366,0.0008778568,0.1679037,0.0003400791,0.001357675,0.00473877,0.00006109308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589378,0.004648352,0.008442857,0.004397548,0.0001332933,0.0005580175,0.01615292,0.0002736181,0.006455626],"genre_scores_gemma":[0.9848011,0.0007493668,0.008814937,0.0002182952,0.00002675087,0.0001188089,0.00456726,0.00001530369,0.000688081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03143185,"threshold_uncertainty_score":0.2190442,"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."}}