{"id":"W4408004265","doi":"10.1177/08968608251317463","title":"Predictive models on patients’ eligibility for peritoneal dialysis","year":2025,"lang":"en","type":"article","venue":"Peritoneal Dialysis International","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Calgary; University of Toronto; University of Waterloo","funders":"Mitacs","keywords":"Medicine; Peritoneal dialysis; Logistic regression; Akaike information criterion; Receiver operating characteristic; Hemodialysis; Retrospective cohort study; Dialysis; Internal medicine; Intensive care unit; Emergency medicine; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008730289,0.001821661,0.001089537,0.003748741,0.0006406258,0.002454041,0.001428737,0.0008593668,0.003273329],"category_scores_gemma":[0.02351897,0.0004850284,0.002155693,0.001706502,0.0006098011,0.0009561895,0.001477472,0.001895344,0.0006282745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001391643,"about_ca_system_score_gemma":0.002197397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01884606,"about_ca_topic_score_gemma":0.01110654,"domain_scores_codex":[0.9974119,0.001244352,0.0001794742,0.0004332685,0.0003960856,0.0003349647],"domain_scores_gemma":[0.9770348,0.0181604,0.002015016,0.0005075588,0.001827286,0.0004549884],"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":[0.0006666053,0.0003927973,0.8199493,0.0001014397,0.0007161848,0.0002949953,0.0002686032,0.1429012,0.0002314969,0.001311186,0.002931853,0.03023443],"study_design_scores_gemma":[0.00005013924,0.000177541,0.08020085,0.0001078622,0.0003203675,0.0002229643,0.0002563404,0.9129246,0.0003190716,0.004416272,0.0009606831,0.00004331507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343815,0.001474233,0.05563933,0.002102055,0.0002180094,0.0002323404,0.002483964,0.0005248397,0.002943771],"genre_scores_gemma":[0.9914955,0.0003229475,0.005965754,0.00008948354,0.00008036153,0.00008326502,0.001316501,0.00002112417,0.0006249971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01884606,"threshold_uncertainty_score":0.04617077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01518413004909404,"score_gpt":0.3143855895392749,"score_spread":0.2992014594901808,"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."}}