{"id":"W3118526518","doi":"10.1101/2020.12.26.20248867","title":"Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Frailty in Older Adults","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Logistic regression; Heart failure; Decision tree; Multivariate statistics; Frailty Index; Comorbidity; Gradient boosting; Internal medicine; Multivariate analysis; Retrospective cohort study; Machine learning; Random forest; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.00401731,0.0006836668,0.0008337756,0.002559688,0.000275825,0.001025635,0.0006552318,0.0007333729,0.00107912],"category_scores_gemma":[0.01250084,0.000183931,0.0009376348,0.001187811,0.00023255,0.0005269435,0.0006790102,0.0007165771,0.0004033804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005242362,"about_ca_system_score_gemma":0.0008240726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002612793,"about_ca_topic_score_gemma":0.002107189,"domain_scores_codex":[0.9988534,0.0006084758,0.0001403476,0.0001343065,0.0001817255,0.00008169104],"domain_scores_gemma":[0.9960809,0.002103969,0.0005988363,0.000206659,0.0008375188,0.0001721132],"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":[0.0006156466,0.0006464332,0.7843681,0.0001369407,0.0004522313,0.0003502127,0.00008728196,0.08409751,0.002223285,0.001480748,0.00195544,0.1235863],"study_design_scores_gemma":[0.0000465624,0.0003844594,0.1846862,0.00008452543,0.0001370301,0.0002001743,0.00008009304,0.8103824,0.0009906377,0.002364967,0.0006094191,0.00003352526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.766059,0.0008162245,0.2282569,0.0006275668,0.0001232262,0.0003441416,0.001492609,0.0003173287,0.001962944],"genre_scores_gemma":[0.9659278,0.0001309128,0.0325404,0.00007903681,0.0000541421,0.0001445164,0.0007794574,0.000009373593,0.0003342868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00401731,"threshold_uncertainty_score":0.02124584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04123907301516067,"score_gpt":0.3196127500674774,"score_spread":0.2783736770523167,"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."}}