{"id":"W4380786167","doi":"10.1161/circ.146.suppl_1.13225","title":"Abstract 13225: Frailty Screening at Scale Using Core Clinical Data and Supervised Machine Learning","year":2022,"lang":"en","type":"article","venue":"Circulation","topic":"Frailty in Older Adults","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"","keywords":"Medicine; Cohort; Population; Prospective cohort study; Gerontology; Cohort study; Standard error; Machine learning; Physical therapy; Statistics; Internal medicine","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":[0.009104138,0.001235575,0.001265828,0.001861559,0.0004974491,0.001339386,0.001932347,0.0008145412,0.003225595],"category_scores_gemma":[0.01954276,0.0003321207,0.001002726,0.001084923,0.0006412752,0.0006627237,0.00140324,0.001115177,0.001115775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075154,"about_ca_system_score_gemma":0.002755262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02053764,"about_ca_topic_score_gemma":0.01633994,"domain_scores_codex":[0.9979086,0.0009862265,0.0001165194,0.0005278925,0.0003516576,0.0001092573],"domain_scores_gemma":[0.9947659,0.002669459,0.000511683,0.0006231987,0.001140065,0.0002897191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001926142,0.001300936,0.3254691,0.0009352224,0.002466655,0.0004340364,0.0003121826,0.2067328,0.002860674,0.001838372,0.03806223,0.4176615],"study_design_scores_gemma":[0.0002355697,0.0005980106,0.08768763,0.0001846281,0.0002537784,0.0002108909,0.00008165798,0.9005283,0.001251767,0.006120607,0.0027762,0.00007100939],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6359696,0.003113674,0.3308905,0.003133564,0.0003004269,0.001468534,0.01397953,0.005725009,0.005419143],"genre_scores_gemma":[0.9198778,0.0003727982,0.06757132,0.0002964817,0.0001404816,0.0005653623,0.00941907,0.0001189008,0.00163782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02053764,"threshold_uncertainty_score":0.04814786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2250727082232752,"score_gpt":0.3876835922406658,"score_spread":0.1626108840173906,"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."}}