{"id":"W4410477942","doi":"10.2196/72349","title":"Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Acute Kidney Injury Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Acute kidney injury; Preprint; Critically ill; Medicine; Intensive care medicine; Disease; Kidney disease; Computer science; Cardiology; Algorithm; Machine learning; Internal medicine; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01084759,0.00114853,0.001153179,0.001540079,0.0004686659,0.0009674324,0.001258603,0.001233907,0.0009833191],"category_scores_gemma":[0.02072951,0.0003138791,0.0008734051,0.0008924647,0.0003966028,0.0007770527,0.001182628,0.00197841,0.0004111365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008404985,"about_ca_system_score_gemma":0.002523963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004297832,"about_ca_topic_score_gemma":0.002295494,"domain_scores_codex":[0.997442,0.001549864,0.0002256752,0.0003157364,0.0003458537,0.0001208139],"domain_scores_gemma":[0.9901428,0.00723119,0.0004935082,0.0003456646,0.001619364,0.0001674933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005339602,0.0005896927,0.04373346,0.0002732879,0.0003679779,0.0001509515,0.0001141146,0.7144122,0.001155957,0.001906432,0.002990039,0.233772],"study_design_scores_gemma":[0.00002164592,0.00006798442,0.001091293,0.00002909784,0.00001707734,0.00002129969,0.00001295642,0.9974195,0.0003276553,0.0008011001,0.0001859164,0.000004502974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2508085,0.003716186,0.738245,0.001449586,0.0001395019,0.0008275376,0.0006169734,0.001964245,0.002232453],"genre_scores_gemma":[0.7271717,0.0008135299,0.2691225,0.0002284099,0.00007294142,0.0008945561,0.001037301,0.00005796062,0.0006011903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01084759,"threshold_uncertainty_score":0.05736822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117861873396322,"score_gpt":0.3158402063601353,"score_spread":0.3040540190205031,"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."}}