{"id":"W4323810109","doi":"10.1186/s12882-023-03093-6","title":"Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models","year":2023,"lang":"en","type":"article","venue":"BMC Nephrology","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Alberta; University of Calgary","funders":"Canadian Institutes of Health Research; Academy of Medical Sciences","keywords":"Medicine; Interquartile range; Dialysis; Population; Myocardial infarction; Internal medicine; Nephrology; Renal function; Cardiac surgery; Heart failure; Hemodialysis; Acute kidney injury; Logistic regression; Retrospective cohort study; Kidney disease; Confidence interval; Surgery; Intensive care medicine; Cardiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004046751,0.00009929245,0.0005966742,0.0001746341,0.00002416651,0.000003205954,0.00002129185,0.00009901208,0.00001443328],"category_scores_gemma":[0.0002303984,0.00007621877,0.0001870649,0.0001821383,0.00005082529,0.0001015136,0.00001711714,0.00005701759,0.000001782114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001458876,"about_ca_system_score_gemma":0.00008301432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007193001,"about_ca_topic_score_gemma":0.00002141839,"domain_scores_codex":[0.9991495,0.0001205117,0.000276166,0.0001755537,0.0001466063,0.0001316595],"domain_scores_gemma":[0.9988726,0.0006532175,0.0001309395,0.000122763,0.0001518893,0.00006860253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00268479,0.00003947668,0.9923834,0.0001531552,0.0002231931,0.000002753924,0.0007519352,0.0001010605,0.002301635,0.0001690633,0.0008513446,0.0003382534],"study_design_scores_gemma":[0.001330563,0.0003091496,0.991598,0.00005363603,0.0003158383,0.00002311789,0.0001329336,0.002486501,0.002972523,0.0001424869,0.000570848,0.00006446399],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827256,0.00002624133,0.01588303,0.0005203651,0.00009310543,0.000567393,0.0001273801,0.00002065756,0.00003627027],"genre_scores_gemma":[0.9978938,0.00005609169,0.001325669,0.0001518978,0.00007056438,0.0001410838,0.0002400256,0.00001466558,0.0001062618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01516819,"threshold_uncertainty_score":0.3108111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01597298750193097,"score_gpt":0.2386336206180659,"score_spread":0.222660633116135,"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."}}