{"id":"W4412465429","doi":"10.1111/nep.70097","title":"Machine Learning Models for Predicting Acute and Chronic Kidney Diseases During the Post‐Covid‐19 Pandemic","year":2025,"lang":"en","type":"letter","venue":"Nephrology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre; McGill Genome Centre","funders":"","keywords":"Medicine; Pandemic; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Intensive care medicine; Coronavirus Infections; Virology; Internal medicine; Infectious disease (medical specialty); Disease; Outbreak","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000467314,0.0005133708,0.0006333523,0.0003251862,0.001171768,0.0001927597,0.001721639,0.0008499704,0.00002730456],"category_scores_gemma":[0.0009416668,0.0004084457,0.0001962093,0.0002574055,0.0001932954,0.0002519048,0.001271532,0.00394672,0.000004565157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003831149,"about_ca_system_score_gemma":0.001317431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004272646,"about_ca_topic_score_gemma":0.00008102369,"domain_scores_codex":[0.9960088,0.0008428159,0.0005171678,0.001268042,0.0003234984,0.001039707],"domain_scores_gemma":[0.9962314,0.001941493,0.00041801,0.0009901715,0.0001339478,0.0002849294],"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":[0.0005460543,0.00006954771,0.1636216,0.01570124,0.002240349,0.00134746,0.005300782,0.0614891,0.0003170262,0.007940194,0.7091491,0.0322775],"study_design_scores_gemma":[0.0006965466,0.0002657768,0.0006316598,0.00007431944,0.0001338797,0.0003300228,0.000003625783,0.4355697,7.466405e-7,0.002622912,0.5593758,0.0002949388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001905783,0.005024616,0.07031677,0.9190577,0.0009495267,0.001331737,0.0003057488,0.00095926,0.0001488456],"genre_scores_gemma":[0.01367463,0.0008142818,0.001979891,0.9751837,0.003704244,0.0005375309,0.0005951344,0.00008473583,0.003425865],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.3740807,"threshold_uncertainty_score":0.9998367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054815248682508,"score_gpt":0.2870139644178079,"score_spread":0.2664658119309828,"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."}}