{"id":"W4405318358","doi":"10.2459/01.jcm.0001096412.99001.39","title":"ASSOCIATION OF SYSTEMIC INFLAMMATORY REACTION AFTER CARDIAC SURGERY WITH INCREASED 30-DAY MORTALITY: A MACHINE LEARNING APPROACH FOR RISK PREDICTION","year":2024,"lang":"en","type":"article","venue":"Journal of Cardiovascular Medicine","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Systemic inflammatory response syndrome; Propensity score matching; Internal medicine; Framingham Risk Score; Retrospective cohort study; Logistic regression; Incidence (geometry); Surgery; Sepsis; Disease","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":[0.005306536,0.0006976859,0.0006626649,0.001811468,0.0002435118,0.0009779031,0.0005486753,0.0005479972,0.0009531311],"category_scores_gemma":[0.007149007,0.0001961434,0.001050394,0.0007486573,0.0002667949,0.0004922769,0.0005288964,0.001273545,0.0002151803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005072221,"about_ca_system_score_gemma":0.000616785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349867,"about_ca_topic_score_gemma":0.001445845,"domain_scores_codex":[0.9986358,0.000808542,0.0001117225,0.0002129543,0.000153577,0.00007745967],"domain_scores_gemma":[0.9955173,0.002959994,0.0008029977,0.0002249538,0.0003381285,0.0001566657],"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":[0.0005798415,0.0003526438,0.8476757,0.0001113789,0.0006560866,0.0001416495,0.00007029354,0.07848261,0.001092535,0.0005247742,0.001117568,0.06919488],"study_design_scores_gemma":[0.00002701312,0.0005005288,0.1405113,0.00006490687,0.0001708901,0.0002723923,0.00005391858,0.8554367,0.0005354934,0.002052071,0.0003470878,0.0000275416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9084687,0.002556761,0.08549356,0.001169448,0.00008881689,0.000135805,0.0008350194,0.0002041031,0.001047799],"genre_scores_gemma":[0.9854231,0.0002973722,0.01352388,0.00005208487,0.00006478481,0.00005198214,0.0004194643,0.000006981449,0.0001604575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005306536,"threshold_uncertainty_score":0.02806401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009257696433829132,"score_gpt":0.2218334840399857,"score_spread":0.2125757876061566,"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."}}