{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003963449,0.0003948663,0.0007940097,0.0007569867,0.0007985347,0.001724711,0.0009262229,0.004932178,0.002993152],"category_scores_gemma":[0.03840569,0.0003046853,0.0006818586,0.0005356113,0.0005903682,0.001739607,0.0007723792,0.008584768,0.001842664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761309,"about_ca_system_score_gemma":0.001860573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415457,"about_ca_topic_score_gemma":0.02817984,"domain_scores_codex":[0.9984901,0.0008070391,0.0001299926,0.0001188381,0.0002107822,0.0002432662],"domain_scores_gemma":[0.9801092,0.01503249,0.0007218632,0.0005162145,0.002492528,0.001127808],"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":[0.001332359,0.0004646714,0.125087,0.0001672982,0.0002759933,0.001844183,0.0003644157,0.01857693,0.0004314079,0.009684505,0.6613736,0.1803976],"study_design_scores_gemma":[0.0009702392,0.001318266,0.07822086,0.001777556,0.0003668582,0.002382243,0.002808229,0.5322657,0.001396582,0.1302009,0.2479858,0.0003067511],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.02807069,0.002402802,0.004283516,0.9458883,0.007515464,0.00004874734,0.001142008,0.0001322216,0.01051611],"genre_scores_gemma":[0.678091,0.009027526,0.01064792,0.2340944,0.04601998,0.0003045387,0.002230908,0.000131931,0.01945184],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01415457,"threshold_uncertainty_score":0.0281443,"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."}}