{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003638053,0.0003274254,0.0006465143,0.0006387211,0.0002010151,0.0001291507,0.0003424387,0.0001341479,0.0002806585],"category_scores_gemma":[0.0004541077,0.0002796222,0.0009944022,0.0004539607,0.0001375052,0.0001859939,0.000172236,0.0001765996,0.00003610065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004769485,"about_ca_system_score_gemma":0.000134276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001018237,"about_ca_topic_score_gemma":0.00002704607,"domain_scores_codex":[0.9971353,0.00007908923,0.0006871755,0.0008390695,0.0009049629,0.0003544196],"domain_scores_gemma":[0.9983109,0.000196791,0.0001630883,0.000535729,0.000591291,0.0002021798],"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":[0.03324627,0.03557984,0.2974357,0.001278462,0.0405908,0.00007975833,0.004515755,0.04050571,0.002765122,0.1240467,0.1340204,0.2859355],"study_design_scores_gemma":[0.01502976,0.001479302,0.4062394,0.0005245695,0.007517506,0.00000155817,0.0005884724,0.4553905,0.003287892,0.01754964,0.09112352,0.00126795],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7855942,0.0001933377,0.04479071,0.007911505,0.00323557,0.002560525,0.001115331,0.0002669537,0.1543318],"genre_scores_gemma":[0.990726,0.00008552227,0.0008539282,0.001686965,0.0006407292,0.0006178312,0.001521167,0.00002836939,0.003839501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4148847,"threshold_uncertainty_score":0.9999656,"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."}}