{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004124325,0.0002833718,0.0005816234,0.0004219747,0.0002354418,0.0000531036,0.0001456309,0.00009275139,0.000001899986],"category_scores_gemma":[0.00003159274,0.0002290351,0.00008692854,0.000412626,0.0001508732,0.0004678642,0.0000755297,0.0002435942,4.403997e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002729749,"about_ca_system_score_gemma":0.0001583844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008651851,"about_ca_topic_score_gemma":0.00007625598,"domain_scores_codex":[0.9981258,0.00004937485,0.0007060815,0.0002436901,0.0004226259,0.0004524207],"domain_scores_gemma":[0.9986278,0.0001914371,0.0002831166,0.0004452315,0.0002422984,0.0002101041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001858146,0.002718632,0.05963198,0.001014506,0.0009746127,0.00002036168,0.4620162,0.4042426,0.00003284663,0.001265658,0.01404628,0.05217811],"study_design_scores_gemma":[0.004299297,0.002072159,0.00088311,0.004199386,0.0003798244,0.00001676288,0.06214446,0.9241463,0.0001491631,0.001329058,0.0001749105,0.0002055708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5383508,0.00003629348,0.4567068,0.001655564,0.0001600374,0.002542587,0.00003342504,0.000104382,0.0004100848],"genre_scores_gemma":[0.7946615,0.000007466645,0.2050451,0.00007248926,0.00009642202,0.00006078147,0.00002051893,0.00002768476,0.000007942214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5199037,"threshold_uncertainty_score":0.9339778,"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."}}