{"id":"W2891248192","doi":"10.23889/ijpds.v3i4.832","title":"Identification of Frailty using EMR and Admin data: A complex issue","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Frailty in Older Adults","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Manitoba; University of British Columbia; Manitoba Health","funders":"","keywords":"Audit; Medical record; Vulnerability (computing); Medicine; Population; Gerontology; Medical emergency; Computer science; Computer security; Environmental health; Business","routes":{"ca_aff":true,"ca_fund":false,"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":[0.07828222,0.0006408616,0.001210173,0.005799798,0.001225884,0.005544995,0.002731297,0.001277427,0.003535938],"category_scores_gemma":[0.1938137,0.0005603696,0.001232089,0.007757919,0.001714413,0.00434993,0.004135266,0.001838277,0.001054867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003160441,"about_ca_system_score_gemma":0.00349832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03360172,"about_ca_topic_score_gemma":0.03416711,"domain_scores_codex":[0.9289595,0.03946941,0.00956455,0.004468854,0.01581031,0.001727274],"domain_scores_gemma":[0.7798388,0.1371671,0.03882615,0.01504756,0.02696994,0.0021504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001097908,0.0000735932,0.8824283,0.0007626091,0.000459997,0.0001451596,0.001260257,0.0008931156,0.0001154359,0.002935919,0.01244481,0.09837095],"study_design_scores_gemma":[0.00002836176,0.0001455263,0.9311711,0.004437033,0.0002081564,0.001169175,0.005350537,0.009747636,0.000615843,0.01026242,0.03671834,0.000145944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6634683,0.04091563,0.09290787,0.1276441,0.002175956,0.001238605,0.03523722,0.0006416976,0.03577066],"genre_scores_gemma":[0.9447501,0.004551526,0.03179394,0.007223288,0.001179956,0.0006328815,0.008300023,0.0001014149,0.001466762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07828222,"threshold_uncertainty_score":0.4140009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.270249451982674,"score_gpt":0.4940552802874872,"score_spread":0.2238058283048132,"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."}}