{"id":"W3049297405","doi":"10.2196/20578","title":"Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hemodialysis; Medicine; Dialysis; Mortality rate; Area under the curve; Internal medicine; Machine learning; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005131805,0.0001410518,0.0003532136,0.00008240859,0.00007279716,0.00003996822,0.00008334699,0.00006373597,0.00003926801],"category_scores_gemma":[0.0005475945,0.0001066061,0.00005965995,0.0002377657,0.0000291226,0.0001689129,0.0001489091,0.0001742762,0.00001016865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006467614,"about_ca_system_score_gemma":0.00006745583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001157492,"about_ca_topic_score_gemma":0.00002976855,"domain_scores_codex":[0.9980111,0.00003728359,0.0007684426,0.000137244,0.000860547,0.0001853706],"domain_scores_gemma":[0.9992383,0.00007145575,0.0001547805,0.0001037774,0.00007186845,0.0003597818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001489316,0.001308153,0.9486534,0.001109088,0.0002644516,0.00001151427,0.02489292,0.001186381,1.886799e-7,0.00006381675,0.0001562531,0.02220492],"study_design_scores_gemma":[0.007806967,0.0007950506,0.424569,0.0002577583,0.0004709292,5.257905e-7,0.005780961,0.5574895,0.00001766506,0.00007322043,0.00246735,0.0002711413],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922029,0.00001645828,0.005180822,0.0003726174,0.00002343584,0.001881983,0.000004100829,0.00003993222,0.0002777724],"genre_scores_gemma":[0.9971942,0.00002124872,0.001499795,0.0006238981,0.00003720821,0.0003136179,0.000290487,0.000009244017,0.0000102997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5563031,"threshold_uncertainty_score":0.4347273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03661181780276673,"score_gpt":0.2939131856849027,"score_spread":0.2573013678821359,"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."}}