{"id":"W4309509889","doi":"10.1111/hdi.13053","title":"Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative","year":2022,"lang":"en","type":"article","venue":"Hemodialysis International","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fresenius Medical Care North America","keywords":"Medicine; Logistic regression; Dialysis; Receiver operating characteristic; Hemodialysis; Predictive modelling; Statistical model; Intensive care medicine; Statistics; Emergency medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006719064,0.0006597121,0.0005552809,0.001566379,0.0002398183,0.00112125,0.0006801235,0.0004273564,0.0005730002],"category_scores_gemma":[0.01347925,0.0001440132,0.001031339,0.00104704,0.0002365019,0.0008122138,0.001099636,0.0008520278,0.0001737558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003920549,"about_ca_system_score_gemma":0.0006795388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002488426,"about_ca_topic_score_gemma":0.002517727,"domain_scores_codex":[0.9978284,0.001198348,0.0001645676,0.0003204838,0.000398793,0.00008938539],"domain_scores_gemma":[0.9941055,0.00377887,0.0009394987,0.0003798976,0.0005824336,0.0002137238],"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":[0.0003436792,0.0001807377,0.9024894,0.0001376048,0.0004939395,0.00007940458,0.0001962183,0.03417576,0.0007436084,0.0006426845,0.001172386,0.05934471],"study_design_scores_gemma":[0.0000572864,0.0008037706,0.4235454,0.0001778344,0.0002879993,0.0003829975,0.0004019411,0.5656708,0.002074612,0.004348094,0.002181968,0.00006720876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9354556,0.00123502,0.05968679,0.0008291217,0.0000473687,0.0001066766,0.001203535,0.0002297329,0.001206157],"genre_scores_gemma":[0.9696925,0.0003759009,0.02835876,0.00007420756,0.00005789082,0.00007061089,0.001177714,0.0000196746,0.0001727032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006719064,"threshold_uncertainty_score":0.03553426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06144151792079218,"score_gpt":0.3433216085509998,"score_spread":0.2818800906302076,"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."}}