{"id":"W4389191886","doi":"10.22215/etd/2023-15775","title":"Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Chronic Kidney Disease and Diabetes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Kingston General Hospital; Ottawa Hospital; University Health Network","funders":"","keywords":"Timeline; Dialysis; Kidney disease; Medicine; Dialysis Therapy; Intensive care medicine; Incidence (geometry); Artificial intelligence; Intervention (counseling); Machine learning; Computer science; Internal medicine; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004987213,0.0006012801,0.000765184,0.0007462939,0.0005196015,0.001456164,0.001170221,0.001308513,0.001924381],"category_scores_gemma":[0.01266483,0.0003833291,0.0006695499,0.0004752201,0.0004400986,0.0006737731,0.0008402822,0.001912986,0.0006046022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441032,"about_ca_system_score_gemma":0.002766042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02197535,"about_ca_topic_score_gemma":0.01066852,"domain_scores_codex":[0.9992091,0.0003516854,0.00006111712,0.0001487504,0.0001343421,0.00009497473],"domain_scores_gemma":[0.9920802,0.005914618,0.0002941466,0.000191788,0.001404189,0.0001151943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004332744,0.00008328986,0.004251372,0.00002904706,0.00003256913,0.00004255933,0.00003049699,0.9676835,0.0003606135,0.00286718,0.001053,0.02352296],"study_design_scores_gemma":[0.000002531188,0.000009785256,0.0002337125,0.000006322936,0.000002545378,0.000002827207,0.000004132675,0.9988029,0.00009736948,0.0007188867,0.0001167937,0.000002111841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1935775,0.0007920958,0.7945516,0.002540756,0.0001860414,0.0003442024,0.001011778,0.0007259592,0.006270141],"genre_scores_gemma":[0.8850942,0.0003450628,0.1087498,0.0002846344,0.00008076846,0.000375467,0.001391514,0.00005554604,0.003623043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02197535,"threshold_uncertainty_score":0.04369485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263716480337193,"score_gpt":0.3133879375152506,"score_spread":0.2870162894815313,"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."}}