{"id":"W3164985898","doi":"10.1002/ehf2.13358","title":"Derivation of an Electronic Frailty Index for Predicting Short-Term Mortality in Heart Failure: A Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"ESC Heart Failure","topic":"Frailty in Older Adults","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Arts Foundation; University of Toronto","funders":"","keywords":"Medicine; Logistic regression; Heart failure; Frailty Index; Decision tree; Gradient boosting; Boosting (machine learning); Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000774129,0.0002985215,0.0006960309,0.0001857039,0.0001249504,0.00003900505,0.0001262218,0.0003203373,0.0001032461],"category_scores_gemma":[0.000700985,0.0003124876,0.0001915305,0.0006246671,0.00007834099,0.000318421,0.00007965514,0.00104166,0.00000365545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002185663,"about_ca_system_score_gemma":0.0004779167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002759925,"about_ca_topic_score_gemma":0.002665808,"domain_scores_codex":[0.99718,0.0002243769,0.0006830101,0.0006856464,0.0005277181,0.0006991761],"domain_scores_gemma":[0.9986975,0.0001302502,0.0001081496,0.0005983197,0.0002636148,0.0002022265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001172252,0.000484398,0.9400875,0.0005716985,0.0001011272,0.00001167216,0.0008080103,0.0007509723,0.05523619,0.0000978793,0.0008672609,0.0008660397],"study_design_scores_gemma":[0.005196247,0.001179413,0.8343235,0.0005902443,0.0001749767,0.0004461623,0.001327187,0.0841288,0.03964699,0.0002292703,0.0321448,0.0006123965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894385,0.0002341815,0.003775064,0.005152912,0.00006728369,0.001076117,0.00003060584,0.0001486016,0.00007673147],"genre_scores_gemma":[0.9770025,0.000006347432,0.02125168,0.0002553727,0.0002493288,0.0001416176,0.0008602426,0.00006843746,0.0001644799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.105764,"threshold_uncertainty_score":0.9999327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508201540817921,"score_gpt":0.3002743406815558,"score_spread":0.2751923252733766,"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."}}