{"id":"W2588103555","doi":"10.1093/ndt/gfw341","title":"Marginal structural models in clinical research: when and how to use them?","year":2017,"lang":"en","type":"article","venue":"Nephrology Dialysis Transplantation","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Marginal structural model; Covariate; Confounding; Medicine; Statistics; Econometrics; Outcome (game theory); Population; Mathematics; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01633903,0.000142716,0.0007618379,0.0004380413,0.0004217303,0.0002981674,0.0003479499,0.0002535607,0.00009408343],"category_scores_gemma":[0.001839634,0.0001719304,0.00008616149,0.00008489537,0.0002414246,0.001310197,0.00003969168,0.0003279369,0.00022576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182586,"about_ca_system_score_gemma":0.0000617169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00211175,"about_ca_topic_score_gemma":0.003912629,"domain_scores_codex":[0.9962489,0.0009135421,0.001652767,0.0006553162,0.0001051618,0.0004243243],"domain_scores_gemma":[0.9966462,0.001752886,0.0007489648,0.0005965594,0.00007460529,0.000180742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000285586,0.00004097886,0.90935,0.0001140238,0.0001004591,0.00001142912,0.006451454,0.0003929341,0.00001731979,0.07947651,0.002752563,0.001006699],"study_design_scores_gemma":[0.001319686,0.0000778199,0.9217182,0.00002415189,0.00001783615,0.00000819158,0.0001574054,0.01245486,0.000003081928,0.06143349,0.002598544,0.0001867369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379712,0.00006557987,0.003896026,0.05657129,0.0002197377,0.000483952,0.0001705021,0.00001832439,0.0006033792],"genre_scores_gemma":[0.9903389,0.0004617678,0.004439895,0.004183852,0.0002207119,0.00008108803,0.00005599184,0.00001792289,0.000199888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05238744,"threshold_uncertainty_score":0.7011119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6043812969454161,"score_gpt":0.4885232630757521,"score_spread":0.115858033869664,"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."}}