{"id":"W3008831871","doi":"10.1097/ee9.0000000000000085","title":"Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models","year":2020,"lang":"en","type":"article","venue":"Environmental Epidemiology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Medical Research Council; Wellcome Trust; U.S. Environmental Protection Agency","keywords":"Covariate; Confounding; Proportional hazards model; Statistics; Inverse probability weighting; Marginal structural model; Weighting; Population; Econometrics; Demography; Medicine; Environmental health; Mathematics; Estimator","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00231968,0.0003481533,0.000628854,0.00001545912,0.0004263141,0.00000889557,0.0002472585,0.0002529257,0.001047661],"category_scores_gemma":[0.0008123921,0.0002831935,0.0001085292,0.0001335325,0.0009812483,0.000645381,0.0003262387,0.0005072559,0.0002498588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005812646,"about_ca_system_score_gemma":0.00003668325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000164303,"about_ca_topic_score_gemma":0.00002557667,"domain_scores_codex":[0.9953204,0.001936572,0.0008050276,0.0008743188,0.0003037653,0.0007598818],"domain_scores_gemma":[0.9980566,0.0006447041,0.0005038466,0.0003302181,0.000002428559,0.0004621929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003403331,0.0003449858,0.7676834,0.0003486739,0.00006495111,0.00002964273,0.003031704,0.1887502,0.002576813,0.001219316,0.001213573,0.0343964],"study_design_scores_gemma":[0.001249712,0.001492958,0.7565814,0.0000732226,0.0000755001,0.00007547133,0.00008556236,0.2151123,0.001303646,0.02283587,0.0004910998,0.0006232532],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6193029,0.0000878491,0.3725367,0.006758931,0.00009563103,0.0007085528,0.00001373852,0.000090043,0.000405705],"genre_scores_gemma":[0.6002531,0.00001387494,0.3865861,0.01287377,0.0001165952,0.00005503267,0.00005606825,0.00002459632,0.00002089824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03377314,"threshold_uncertainty_score":0.999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1020836255498205,"score_gpt":0.3665521475640303,"score_spread":0.2644685220142099,"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."}}