{"id":"W3132535830","doi":"10.14740/cr1220","title":"A Dashboard for Tracking Mortality After Cardiac Surgery Using a National Administrative Database","year":2021,"lang":"en","type":"article","venue":"Cardiology Research","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Dashboard; National database; Cardiac surgery; Metric (unit); Quality management; Mortality rate; Medical emergency; Data quality; Emergency medicine; Database; Surgery; Operations management; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002190178,0.0001280075,0.000698309,0.000152158,0.0001480111,0.00003006004,0.00004686296,0.0001322297,0.0000916137],"category_scores_gemma":[0.00226477,0.0001130568,0.0004360567,0.0002928317,0.0002241531,0.00007066895,0.00008017889,0.0002642958,0.00001569926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595859,"about_ca_system_score_gemma":0.001581055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002015963,"about_ca_topic_score_gemma":0.000009185543,"domain_scores_codex":[0.9976689,0.0005794345,0.000246089,0.0005109577,0.0005285413,0.0004660533],"domain_scores_gemma":[0.9958207,0.00218111,0.0000308763,0.0003684468,0.001429143,0.0001697065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003740703,0.0002305914,0.9709365,0.0001524141,0.001727522,0.001910745,0.00009044827,0.000006354353,0.00371631,0.0003889379,0.01960005,0.0008660135],"study_design_scores_gemma":[0.001164568,0.0002584181,0.9490657,0.0002445567,0.0005218146,0.0004330034,0.000792085,0.0002135507,0.02062509,0.0004134288,0.02598658,0.000281135],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922387,0.002802921,0.0001424129,0.0009655217,0.0002587856,0.0006224471,0.0009670506,0.000020844,0.001981327],"genre_scores_gemma":[0.9965057,0.0002035225,0.0007164007,0.0002517509,0.0006952246,0.0007252847,0.0008078614,0.00002079587,0.00007342607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02187077,"threshold_uncertainty_score":0.4610324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7325837591917839,"score_gpt":0.579426978184974,"score_spread":0.1531567810068099,"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."}}