{"id":"W2792390571","doi":"10.1177/0962280218756159","title":"Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institute for Clinical Evaluative Sciences; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care","keywords":"Propensity score matching; Covariate; Statistics; Observational study; Logistic regression; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.04939106,0.001020272,0.00134821,0.002919559,0.0007410435,0.002218956,0.001283729,0.001219855,0.005084946],"category_scores_gemma":[0.1900874,0.0005192388,0.00174909,0.003340574,0.002007918,0.003422161,0.003240511,0.002001496,0.0004494003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055615,"about_ca_system_score_gemma":0.002809083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002942639,"about_ca_topic_score_gemma":0.002722439,"domain_scores_codex":[0.9640628,0.02813017,0.001806698,0.00230595,0.003352868,0.0003415748],"domain_scores_gemma":[0.8966837,0.08162999,0.00946182,0.009067093,0.00276502,0.0003923633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000606958,0.0002395839,0.08360512,0.001576329,0.0025944,0.0004513059,0.001665786,0.158999,0.002917461,0.3188435,0.006946823,0.4215536],"study_design_scores_gemma":[0.000254834,0.0005792576,0.02882395,0.000565997,0.0004796816,0.0004815218,0.0004697988,0.2833111,0.002869125,0.6558675,0.02615275,0.0001444371],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01217602,0.0004061151,0.9851006,0.0003497907,0.00004171034,0.0002814207,0.0002815318,0.0002039243,0.001158948],"genre_scores_gemma":[0.2411635,0.00106687,0.753661,0.0004365823,0.0001566288,0.001394987,0.0009909732,0.000181473,0.0009479245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04939106,"threshold_uncertainty_score":0.261208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7850233477568593,"score_gpt":0.6838532647255835,"score_spread":0.1011700830312758,"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."}}