{"id":"W4234367736","doi":"10.23889/ijpds.v3i4.829","title":"Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living With Frailty?","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","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":"Medicine; Medical record; Electronic medical record; Population; Vulnerability (computing); Medical diagnosis; Gerontology; Primary care; Health care; Medical emergency; Family medicine; Environmental health; Internal medicine; Computer security","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":[0.009902298,0.000528038,0.000715077,0.006858953,0.0006930445,0.002994733,0.00180765,0.001224564,0.006400169],"category_scores_gemma":[0.07464798,0.0003059912,0.0008787681,0.01198919,0.0004197353,0.00249585,0.001963045,0.001004096,0.001546814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179146,"about_ca_system_score_gemma":0.003612874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0794116,"about_ca_topic_score_gemma":0.1023416,"domain_scores_codex":[0.9934493,0.002483345,0.0009840644,0.0006863943,0.001679363,0.0007177265],"domain_scores_gemma":[0.9591945,0.01609336,0.01137956,0.003079487,0.008540214,0.001712852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001054555,0.00009921286,0.8709071,0.0006137959,0.0003012621,0.00007092614,0.0003659169,0.0003486689,0.0000575304,0.0009057463,0.03594377,0.09028058],"study_design_scores_gemma":[0.0001289357,0.0001019563,0.9114764,0.004561542,0.0003829388,0.0003068106,0.001742925,0.009063335,0.0003367589,0.006255028,0.06554476,0.00009861923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5993585,0.02997756,0.01546833,0.1169358,0.001721064,0.001083713,0.1953942,0.0007801322,0.03928063],"genre_scores_gemma":[0.862438,0.01104805,0.02491856,0.01129769,0.001627495,0.0009329468,0.08474432,0.0001077927,0.00288525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0794116,"threshold_uncertainty_score":0.1578987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2008263463979776,"score_gpt":0.5331611133300562,"score_spread":0.3323347669320786,"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."}}