{"id":"W2907387021","doi":"10.1097/cin.0000000000000499","title":"Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions","year":2018,"lang":"en","type":"article","venue":"CIN Computers Informatics Nursing","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Burman University","funders":"","keywords":"Statistic; Medicine; Predictive modelling; Emergency medicine; Heart failure; Cohort; Hospital readmission; Framingham Risk Score; Psychological intervention; Retrospective cohort study; Intensive care medicine; Medical emergency; Statistics; Disease; Internal medicine; Mathematics","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":[0.03689185,0.002394685,0.001784532,0.002582677,0.001059929,0.003893317,0.00224075,0.001669968,0.001544414],"category_scores_gemma":[0.1254865,0.001197306,0.002500998,0.002232659,0.001832069,0.008547147,0.00246464,0.005317574,0.0006463983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302676,"about_ca_system_score_gemma":0.001721212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007961822,"about_ca_topic_score_gemma":0.01253965,"domain_scores_codex":[0.9787211,0.01791168,0.0005961682,0.001191744,0.001305489,0.0002738338],"domain_scores_gemma":[0.8362122,0.1472757,0.003142609,0.009670746,0.00268687,0.001011827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002718857,0.001067411,0.155921,0.001047472,0.003161211,0.0005096757,0.003305139,0.4053923,0.002491861,0.02035989,0.01002105,0.3940041],"study_design_scores_gemma":[0.0001883376,0.000749174,0.01445724,0.0002970688,0.0004383084,0.0002096989,0.0005071936,0.9349596,0.0008273953,0.04410002,0.003166239,0.00009966116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5060345,0.008188127,0.433499,0.04473557,0.0004579708,0.000321143,0.0009502032,0.001720666,0.004092875],"genre_scores_gemma":[0.7979925,0.002451389,0.1938124,0.002547991,0.0004439887,0.0001157168,0.001193806,0.0004047742,0.001037388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03689185,"threshold_uncertainty_score":0.1951051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03297899621240772,"score_gpt":0.2966315945382079,"score_spread":0.2636525983258002,"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."}}