{"id":"W4281263407","doi":"10.1109/jbhi.2022.3175862","title":"Marginal Structural Models Using Calibrated Weights With SuperLearner: Application to Type II Diabetes Cohort","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; North York General Hospital; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Type 2 diabetes; Machine learning; Medicine; Artificial intelligence; Context (archaeology); Causal inference; Metformin; Population; Diabetes mellitus; Computer science; Statistics; Mathematics; Endocrinology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02744287,0.001061222,0.002099909,0.001465802,0.00099546,0.001585277,0.004473113,0.002431298,0.005831827],"category_scores_gemma":[0.05207665,0.001000171,0.003309466,0.001693648,0.001547275,0.002324929,0.002590728,0.00452656,0.0006649614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002269275,"about_ca_system_score_gemma":0.0028258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03842468,"about_ca_topic_score_gemma":0.03164103,"domain_scores_codex":[0.9959663,0.00305175,0.0001295965,0.0004564849,0.0002171224,0.0001785928],"domain_scores_gemma":[0.9608902,0.03259239,0.001353957,0.002643413,0.001901431,0.0006185989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003775581,0.0001824462,0.01228774,0.00008478873,0.0003326084,0.0002732265,0.0003056792,0.9136881,0.0002324968,0.03796968,0.001583197,0.03268239],"study_design_scores_gemma":[0.00004431741,0.00002588379,0.0004262487,0.00001272399,0.00002177967,0.0000173998,0.00002034419,0.979333,0.00008458236,0.01967417,0.0003247421,0.00001473002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1176342,0.0004613,0.8775684,0.0009765973,0.0001002708,0.0003107703,0.0008847316,0.000913177,0.00115052],"genre_scores_gemma":[0.6117762,0.0003160428,0.3808793,0.0005245602,0.0001178561,0.0007733828,0.001506093,0.0002767355,0.003829858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03842468,"threshold_uncertainty_score":0.1451335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008868492847084,"score_gpt":0.38704451659541,"score_spread":0.2861576673107016,"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."}}