{"id":"W2084602542","doi":"10.1097/mlr.0b013e3181e419fd","title":"Using Administrative Datasets to Study Outcomes in Dialysis Patients","year":2010,"lang":"en","type":"article","venue":"Medical Care","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; St. Michael's Hospital; Western University; Health Sciences Centre; London Health Sciences Centre; University of Calgary; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research","keywords":"Dialysis; Medicine; Baseline (sea); Medical record; Intensive care medicine; Modality (human–computer interaction); Health care; Emergency medicine; Computer science; Internal medicine","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":[0.0416496,0.0005138128,0.0006479819,0.004352349,0.001086156,0.001925358,0.001592006,0.0005853162,0.0007123995],"category_scores_gemma":[0.1483394,0.0003917978,0.0007630872,0.0087533,0.001001826,0.0009528392,0.001928813,0.000990782,0.0001558997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003343917,"about_ca_system_score_gemma":0.007286315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1156235,"about_ca_topic_score_gemma":0.0946331,"domain_scores_codex":[0.9541844,0.03533302,0.002918675,0.001555337,0.005349641,0.000658976],"domain_scores_gemma":[0.7817551,0.131232,0.04712274,0.02462465,0.01348691,0.001778516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002071693,0.0002006604,0.9633407,0.0002880126,0.0009509775,0.00009646891,0.0009406093,0.008184455,0.0001134505,0.003712739,0.002564696,0.0194],"study_design_scores_gemma":[0.0002180952,0.0002954635,0.946357,0.0004032674,0.0004280647,0.0001817524,0.001255826,0.02948375,0.0008528152,0.009135382,0.01132089,0.00006758214],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8697575,0.001818102,0.06333301,0.002961389,0.0001358383,0.001977279,0.05345485,0.0002218577,0.006340157],"genre_scores_gemma":[0.9394779,0.0005753792,0.03370322,0.0003381857,0.00009071078,0.001321565,0.02422282,0.00002154745,0.000248593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1156235,"threshold_uncertainty_score":0.2299011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2353966847959603,"score_gpt":0.5217783212771887,"score_spread":0.2863816364812284,"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."}}