{"id":"W4404869222","doi":"10.1038/s41598-024-80900-6","title":"Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Korean Society of Nephrology; Seoul National University Hospital; Ministry of Science and ICT, South Korea; Korea Health Industry Development Institute; Institute for Information and Communications Technology Promotion; Ewha Womans University; Seoul National University","keywords":"Hemodialysis; Prospective cohort study; Medicine; Cohort; Cohort study; Intensive care medicine; Internal medicine; Pediatrics","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":[0.009144916,0.0002318122,0.0004282269,0.0009188026,0.001062098,0.0001905331,0.0001566111,0.0002704632,0.00008542297],"category_scores_gemma":[0.00342172,0.0002246432,0.0001106132,0.003000024,0.0002097193,0.0005830572,0.0001795198,0.001423673,0.00004669874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214623,"about_ca_system_score_gemma":0.0008388783,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009375948,"about_ca_topic_score_gemma":0.007171326,"domain_scores_codex":[0.9937464,0.00094837,0.001947804,0.001495389,0.001007707,0.0008543444],"domain_scores_gemma":[0.9977662,0.0003276533,0.0005389581,0.0006093049,0.0005919303,0.0001659678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009197184,0.00007679815,0.9316587,0.0002501485,0.0000203181,0.0001251807,0.0623167,0.004756676,0.0000549502,0.00007699749,0.000008536615,0.0006458007],"study_design_scores_gemma":[0.0000762683,0.00001807313,0.7391438,0.0006061875,0.00003398443,0.000004201519,0.0107693,0.2448149,0.00008897229,0.004143407,0.00007143764,0.000229381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882089,0.0001525646,0.001186766,0.00002119561,0.004980071,0.002581889,0.000005833149,0.0002247125,0.002638067],"genre_scores_gemma":[0.9982981,0.000001962376,0.000513513,0.00001500972,0.0001661153,0.0004569096,0.00008744549,0.00004398622,0.0004169269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2400583,"threshold_uncertainty_score":0.9972207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08245882553795787,"score_gpt":0.4089204700094264,"score_spread":0.3264616444714686,"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."}}