{"id":"W2344625650","doi":"10.1016/j.jval.2015.03.086","title":"Comparing the predictive performance of two variants of the elixhauser comorbidity measures for all-cause in-hospital mortality in a large multi-payer u.s. Administrative database","year":2015,"lang":"en","type":"article","venue":"Value in Health","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Comorbidity; Medicine; Confounding; Logistic regression; Emergency medicine; Statistic; Demography; Health care; Medical diagnosis; Internal medicine; Statistics","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":[0.009590408,0.0001607474,0.0004206796,0.0001255081,0.0001747852,0.00001864364,0.0006183379,0.00006085123,0.000002711541],"category_scores_gemma":[0.0005298453,0.0001166947,0.00008059627,0.0007251005,0.0004408309,0.000299388,0.000162416,0.0003272478,7.10585e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002969883,"about_ca_system_score_gemma":0.0007631491,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07961103,"about_ca_topic_score_gemma":0.1388323,"domain_scores_codex":[0.996154,0.001380244,0.0008236821,0.0003300161,0.0007703183,0.0005418044],"domain_scores_gemma":[0.9984636,0.0002382117,0.0005051302,0.0005057566,0.0001841227,0.0001031419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009603088,0.0008661073,0.9595337,0.0001399851,0.00005193391,0.000001839894,0.0284496,0.003434166,0.000002218471,0.007299923,0.00008259777,0.00004190472],"study_design_scores_gemma":[0.001894976,0.0001705401,0.9660277,0.0002283342,0.00002115774,1.379424e-7,0.006939113,0.02382362,0.00003521941,0.0005528629,0.0001918479,0.0001144273],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952577,0.0002093682,0.0001846401,0.000534719,0.0005087641,0.002586017,0.0002343871,0.00001448821,0.0004698515],"genre_scores_gemma":[0.9990476,0.0002991195,0.0003467673,0.00009610027,0.00004943418,0.000133644,0.000009499174,0.00001121467,0.000006646143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05922132,"threshold_uncertainty_score":0.9265179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2333015505398441,"score_gpt":0.4206595807583505,"score_spread":0.1873580302185065,"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."}}