{"id":"W3028704288","doi":"10.3390/informatics7020018","title":"Machine Learning for Identifying Medication-Associated Acute Kidney Injury","year":2020,"lang":"en","type":"article","venue":"Informatics","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Acute kidney injury; Medical prescription; Health care; Harm; Intensive care medicine; Logistic regression; Pharmacoepidemiology; Polypharmacy; MEDLINE; Retrospective cohort study; Health informatics; Population; Medical emergency; Emergency medicine; Data mining; Internal medicine; Public health; Environmental health; Computer science; Pharmacology; Nursing","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":[0.0004642972,0.0001556985,0.0003225587,0.0001242643,0.0001852911,0.00006050677,0.0002229147,0.0001412187,0.0003314294],"category_scores_gemma":[0.005067127,0.0001385768,0.0001168609,0.000382876,0.0000635736,0.0003275444,0.0001285413,0.0005415528,0.0003223133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114795,"about_ca_system_score_gemma":0.0004015428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006721325,"about_ca_topic_score_gemma":1.980152e-7,"domain_scores_codex":[0.9982618,0.00002811153,0.0006289985,0.0001079107,0.0005987951,0.0003743975],"domain_scores_gemma":[0.998428,0.0001018677,0.0002481506,0.0001970838,0.000268944,0.0007559636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006979072,0.0001267718,0.01730632,0.001436731,0.001225374,0.00001805038,0.01424093,0.000005224132,0.004645266,0.0004334407,0.9474753,0.01238865],"study_design_scores_gemma":[0.005357921,0.001345381,0.001066798,0.0002593138,0.0005544151,0.00003657472,0.0009156208,0.330287,0.006371382,0.0001802368,0.6532345,0.0003907944],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852953,0.0004340034,0.3598861,0.3150727,0.002644887,0.01429106,0.01044798,0.005028549,0.1068994],"genre_scores_gemma":[0.9042763,0.0004256777,0.02739233,0.05395027,0.000615301,0.0003139149,0.008896007,0.0001343972,0.003995842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.718981,"threshold_uncertainty_score":0.6066191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05269074474481727,"score_gpt":0.3646401468777998,"score_spread":0.3119494021329825,"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."}}