{"id":"W2791924225","doi":"10.1002/sim.7615","title":"Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Work & Health; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Propensity score matching; Covariate; Statistics; Confounding; Mathematics; Ordinary least squares; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1003104,0.001225222,0.001414135,0.003502361,0.0006272302,0.001651598,0.001397686,0.001630279,0.002778472],"category_scores_gemma":[0.3201249,0.0005258138,0.002696043,0.004267148,0.001991921,0.002336774,0.002219621,0.001547728,0.0003117551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099224,"about_ca_system_score_gemma":0.002949122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005430653,"about_ca_topic_score_gemma":0.003959652,"domain_scores_codex":[0.9321754,0.059082,0.002059848,0.002627303,0.003679029,0.0003765985],"domain_scores_gemma":[0.6710736,0.2933722,0.01199118,0.01812146,0.004803966,0.0006376775],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001625893,0.0004716489,0.1990252,0.002091007,0.007928977,0.0004329727,0.001756356,0.3814577,0.002502694,0.1201783,0.004563851,0.2779655],"study_design_scores_gemma":[0.0006025538,0.002245019,0.08703785,0.0007541511,0.001372654,0.0006342769,0.0005202722,0.7284478,0.003357303,0.1663389,0.008441861,0.0002473652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1022515,0.001189825,0.8924076,0.0006218845,0.00006969836,0.0005410864,0.000581627,0.0002860397,0.002050768],"genre_scores_gemma":[0.4907024,0.001309559,0.5052186,0.000236753,0.0000951063,0.0009372741,0.0008335895,0.0001952027,0.000471597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8996896,"threshold_uncertainty_score":0.5304987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.215693518484249,"score_gpt":0.4913806844402001,"score_spread":0.2756871659559511,"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."}}