{"id":"W4307386048","doi":"10.1038/s41746-022-00710-w","title":"Predicting radiocephalic arteriovenous fistula success with machine learning","year":2022,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Central Venous Catheters and Hemodialysis","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Brigham and Women's Hospital; U.S. Department of Health and Human Services","keywords":"Medicine; Arteriovenous fistula; Receiver operating characteristic; Hemodialysis; Decision tree; Logistic regression; Random forest; Logistic model tree; Machine learning; Radiology; Computer science; Surgery; Internal medicine","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004387112,0.0002159732,0.0004824988,0.0001455605,0.001743193,0.00001914584,0.0002417328,0.00005277264,0.002360194],"category_scores_gemma":[0.0001523259,0.0001588525,0.00006984142,0.0004139034,0.0001113276,0.0001749195,0.0001861,0.001047653,0.00004219958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002767878,"about_ca_system_score_gemma":0.0001250026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006405228,"about_ca_topic_score_gemma":0.000098468,"domain_scores_codex":[0.9977102,0.0001851649,0.0004775171,0.0003658584,0.0006114268,0.0006497759],"domain_scores_gemma":[0.9988467,0.000306043,0.0002759845,0.0002728992,0.00006754018,0.0002308148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005248235,0.0003626898,0.9445035,0.0002222491,0.0002050719,0.000289566,0.02438335,0.0004192323,0.000343333,0.0003306145,0.002637722,0.02577788],"study_design_scores_gemma":[0.0156584,0.01115041,0.1038889,0.001010663,0.0005950541,0.0005303897,0.03962426,0.01006358,0.00002511628,0.0005320198,0.8154909,0.001430348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541449,0.0005718995,0.0002391761,0.002561619,0.0005065057,0.0005749215,0.0001017225,0.0002547372,0.04104451],"genre_scores_gemma":[0.992673,0.00003119914,0.00002107707,0.0009815593,0.0005796343,0.0001301638,0.0003671141,0.00005411421,0.00516209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8406146,"threshold_uncertainty_score":0.9995564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276550189376183,"score_gpt":0.3052088456266984,"score_spread":0.2824433437329366,"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."}}