{"id":"W2883783184","doi":"10.1177/1129729818786630","title":"Improving precision in prediction: Using kidney failure risk equations as a potential adjunct to vascular access planning","year":2018,"lang":"en","type":"article","venue":"The Journal of Vascular Access","topic":"Central Venous Catheters and Hemodialysis","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network","funders":"","keywords":"Vascular access; Medicine; Intensive care medicine; Psychological intervention; Referral; Kidney disease; Renal function; Risk assessment; Risk analysis (engineering); Kidney; Computer science; Surgery; Internal medicine; Hemodialysis; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03887918,0.001629975,0.001940997,0.002890117,0.0005096331,0.004800681,0.001571071,0.001428792,0.002598827],"category_scores_gemma":[0.1772205,0.0009407633,0.001481617,0.002643772,0.0006471225,0.002869305,0.00258913,0.004056877,0.0009194117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009446015,"about_ca_system_score_gemma":0.003046945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415591,"about_ca_topic_score_gemma":0.01293217,"domain_scores_codex":[0.9694475,0.02211786,0.001873963,0.001989322,0.004186088,0.0003853581],"domain_scores_gemma":[0.8322787,0.1353116,0.01163346,0.009433439,0.01033274,0.001010125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001050397,0.0007717363,0.3791358,0.0007101517,0.00170946,0.0001887644,0.001007215,0.03446842,0.0006802026,0.005903695,0.0142009,0.5601733],"study_design_scores_gemma":[0.0007358111,0.001886321,0.3287663,0.003484794,0.002399164,0.001330367,0.001048306,0.5459626,0.004101965,0.06760063,0.04197973,0.0007040765],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1749146,0.01687396,0.7488011,0.02234148,0.001666202,0.001063751,0.003367706,0.003432425,0.02753885],"genre_scores_gemma":[0.6035594,0.004076295,0.385708,0.002396376,0.0009634697,0.0002934896,0.001235867,0.0002351942,0.001531721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03887918,"threshold_uncertainty_score":0.2056152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05228544788217192,"score_gpt":0.3934871937379798,"score_spread":0.3412017458558079,"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."}}