{"id":"W1953676460","doi":"10.6000/1929-6029.2015.04.02.7","title":"Using Propensity Score Matching in Clinical Investigations: A Discussion and Illustration","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Propensity score matching; Observational study; Matching (statistics); Randomized controlled trial; Computer science; Percutaneous coronary intervention; Conventional PCI; Medicine; Internal medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1006167,0.001554687,0.002121398,0.006533852,0.00161363,0.005459166,0.004242796,0.008066099,0.003351897],"category_scores_gemma":[0.1341961,0.001045238,0.002847374,0.009377269,0.01078137,0.006543349,0.004204204,0.01149511,0.001230576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711615,"about_ca_system_score_gemma":0.004398296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002750583,"about_ca_topic_score_gemma":0.002268744,"domain_scores_codex":[0.929819,0.05902098,0.004144187,0.001928033,0.004647887,0.0004398298],"domain_scores_gemma":[0.8712616,0.1182819,0.003200958,0.002733302,0.004106166,0.0004160357],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007754267,0.00007275546,0.0023745,0.005257128,0.0002845418,0.0009612371,0.001139521,0.00433808,0.000312549,0.8353381,0.02546428,0.1243797],"study_design_scores_gemma":[0.00008571346,0.0001793971,0.001745801,0.007277085,0.0001451323,0.002401594,0.0004319908,0.01114311,0.000458815,0.8305512,0.1454425,0.0001376642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001823314,0.2430288,0.6142172,0.1245896,0.004662345,0.000594711,0.0002227981,0.0002014002,0.01065988],"genre_scores_gemma":[0.05430177,0.3457131,0.5487906,0.02946923,0.01648876,0.00245461,0.0002239706,0.0002002901,0.002357629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8993832,"threshold_uncertainty_score":0.5321186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7586343223110931,"score_gpt":0.6327789393899821,"score_spread":0.125855382921111,"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."}}