{"id":"W3083800969","doi":"10.1016/j.jval.2020.06.009","title":"How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data","year":2020,"lang":"en","type":"article","venue":"Value in Health","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université Laval","funders":"Xinjiang University","keywords":"Computer science; Medicine; Emergency 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008556346,0.0002122188,0.0004378807,0.00008835892,0.00006780407,0.00004733763,0.0002138817,0.00007709958,0.0001431842],"category_scores_gemma":[0.001675251,0.0001817024,0.00003534544,0.0003018325,0.00002546069,0.000321583,0.0002456298,0.0005664246,0.00003831551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002065197,"about_ca_system_score_gemma":0.000454134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005460423,"about_ca_topic_score_gemma":0.000192868,"domain_scores_codex":[0.9976348,0.0003920857,0.0004615446,0.0007013705,0.000396292,0.0004138518],"domain_scores_gemma":[0.9984371,0.0004295782,0.0001688918,0.0004903834,0.00002268749,0.0004513669],"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":[0.0001836172,0.0004972616,0.953167,0.0005222367,0.0002399319,0.000879175,0.02349663,0.0001248705,0.0001281919,0.00006982557,0.009648314,0.01104296],"study_design_scores_gemma":[0.01049949,0.01226332,0.3143592,0.01118664,0.0004668029,0.00001489338,0.01247146,0.4062865,0.0006570028,0.0001569517,0.2305787,0.00105908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4279298,0.00363696,0.0003327876,0.5655054,0.0001708696,0.001926183,0.0001142399,0.0001891335,0.0001945945],"genre_scores_gemma":[0.9914057,0.001563452,0.00395452,0.002147335,0.000231402,0.0000266421,0.0002981961,0.00002924432,0.0003434973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6388078,"threshold_uncertainty_score":0.8254563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2222132342234124,"score_gpt":0.3845053589216418,"score_spread":0.1622921246982295,"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."}}