{"id":"W4416427728","doi":"10.1016/j.health.2025.100438","title":"An investigation of treatment barriers for End-Stage Kidney Disease patients using advanced analytics","year":2025,"lang":"en","type":"article","venue":"Healthcare Analytics","topic":"Dialysis and Renal Disease Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Analytics; Kidney disease; Emergency department; Predictive analytics; Dialysis; Disease; Medical decision making","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":[0.007284976,0.0005136325,0.0004392796,0.001884323,0.0006446314,0.002458119,0.001041192,0.0006643156,0.002055377],"category_scores_gemma":[0.03958521,0.0003096124,0.0009420454,0.001984676,0.0008120377,0.002374127,0.002120181,0.001911495,0.0001532624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860393,"about_ca_system_score_gemma":0.004819412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01362305,"about_ca_topic_score_gemma":0.01295642,"domain_scores_codex":[0.9946607,0.003717452,0.0002217461,0.0003315282,0.0006009782,0.0004676898],"domain_scores_gemma":[0.9478325,0.04350816,0.004136447,0.001397004,0.001862471,0.001263423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003995742,0.001959822,0.8117019,0.0003836854,0.0003251852,0.0004144856,0.004201771,0.1114359,0.0005049441,0.02976556,0.003323659,0.03558346],"study_design_scores_gemma":[0.0000600309,0.0007149855,0.1159615,0.000278892,0.0001141344,0.0001887891,0.01389064,0.8400909,0.0008778697,0.02269778,0.005020192,0.000104312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771113,0.0002810913,0.01372453,0.003752786,0.00003727687,0.0002237161,0.0007297375,0.00004678145,0.004092817],"genre_scores_gemma":[0.99198,0.0001599197,0.007039479,0.0001681259,0.00001735408,0.00008170377,0.0003474606,0.00001103231,0.000195028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01362305,"threshold_uncertainty_score":0.03852707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03676623571671684,"score_gpt":0.3562950592781038,"score_spread":0.319528823561387,"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."}}