{"id":"W3090836172","doi":"10.1097/ede.0000000000001253","title":"Postmyocardial Infarction Statin Exposure and the Risk of Stroke with Weighting for Outcome Misclassification","year":2020,"lang":"en","type":"article","venue":"Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; University of Ottawa","funders":"Biogen; Eli Lilly and Company; Canadian Institutes of Health Research; Mitacs; McGill University; Amgen","keywords":"Medicine; Confidence interval; Confounding; Observational study; Inverse probability weighting; Odds ratio; Randomized controlled trial; Stroke (engine); Statin; Inverse probability; Outcome (game theory); Statistics; Internal medicine; Emergency medicine; Propensity score matching; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00183939,0.0001065815,0.0004709863,0.00002236926,0.00006776612,0.000002636956,0.00008674646,0.00008851709,0.000005756762],"category_scores_gemma":[0.01638568,0.00006119296,0.00006225515,0.00005175954,0.0002411377,0.0000555728,0.00003004962,0.0001873908,6.347686e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001346505,"about_ca_system_score_gemma":0.00001550332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002521605,"about_ca_topic_score_gemma":0.00001003332,"domain_scores_codex":[0.9984898,0.0005922898,0.0005237809,0.0001879678,0.00005371418,0.0001524268],"domain_scores_gemma":[0.9885832,0.01050034,0.0006120942,0.0001724166,0.00009224629,0.00003967972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001093834,0.00002305056,0.2487912,0.0001665639,0.0001973264,2.17914e-7,0.002434528,0.0001066251,0.003524143,0.7337894,0.00117059,0.008702572],"study_design_scores_gemma":[0.002583501,0.001196387,0.04465351,0.00003094515,0.0002710879,0.000008490707,0.0009836388,0.007826379,0.002198389,0.9384629,0.001551733,0.0002330518],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2691731,0.000103724,0.7202284,0.009307729,0.00003403395,0.000736922,0.00006334955,0.0001091254,0.000243725],"genre_scores_gemma":[0.77186,0.00005361687,0.2272755,0.000588249,0.00007769831,0.0001057708,0.000008158505,0.00001331346,0.00001765907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.502687,"threshold_uncertainty_score":0.9918997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2210922716130339,"score_gpt":0.4148833491069785,"score_spread":0.1937910774939446,"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."}}