{"id":"W2937710358","doi":"10.1177/0962280219842362","title":"Comparing the high-dimensional propensity score for use with administrative data with propensity scores derived from high-quality clinical data","year":2019,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Sunnybrook Hospital; University of Toronto","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; Confounding; Medicine; Hazard ratio; Proxy (statistics); Statistics; Internal medicine; Mathematics; Confidence interval","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":[0.1555071,0.001501525,0.002285079,0.004823865,0.0008100982,0.004594912,0.002644901,0.001476437,0.008601874],"category_scores_gemma":[0.439374,0.0008103344,0.005597482,0.009547222,0.001726066,0.004506249,0.0051619,0.003049389,0.001053827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001800399,"about_ca_system_score_gemma":0.002641175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004971913,"about_ca_topic_score_gemma":0.002977387,"domain_scores_codex":[0.7637499,0.2066049,0.009401776,0.007572363,0.01153016,0.001140928],"domain_scores_gemma":[0.5922493,0.3232458,0.02995865,0.03851245,0.01414945,0.001884221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008913641,0.001152041,0.5591396,0.002229408,0.04061121,0.0003020159,0.00155548,0.0675488,0.0006371497,0.04533185,0.01440248,0.2581764],"study_design_scores_gemma":[0.005482201,0.006594605,0.4648247,0.001451158,0.008822635,0.0009377317,0.001731412,0.3778791,0.001754082,0.09263455,0.03715437,0.0007334359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2842273,0.003170917,0.6890529,0.003145909,0.000910259,0.003617599,0.008342335,0.0008389206,0.006693829],"genre_scores_gemma":[0.8178158,0.001081662,0.169016,0.000783068,0.0003107222,0.003120899,0.006680466,0.0003715192,0.0008199891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1555071,"threshold_uncertainty_score":0.8224102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.899460556396185,"score_gpt":0.6935688475760646,"score_spread":0.2058917088201204,"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."}}