{"id":"W2159968952","doi":"10.1002/sim.3243","title":"Discussion of ‘A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003’","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"Institute for Clinical Evaluative Sciences","keywords":"Critical appraisal; Matching (statistics); Library science; Citation; Propensity score matching; Psychology; Operations research; Computer science; Medicine; Alternative medicine; Mathematics; Pathology; Surgery","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":["metaresearch"],"category_scores_codex":[0.369296,0.001060695,0.001786455,0.01898471,0.003929027,0.01251598,0.01069203,0.02346361,0.005054329],"category_scores_gemma":[0.7181724,0.001470384,0.005805622,0.01748433,0.01653653,0.01335365,0.005434954,0.01587682,0.0007538879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01325263,"about_ca_system_score_gemma":0.03097724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03032178,"about_ca_topic_score_gemma":0.04726707,"domain_scores_codex":[0.7368293,0.1525751,0.04086598,0.01349428,0.05142085,0.00481444],"domain_scores_gemma":[0.2015161,0.6899145,0.04865548,0.01138519,0.04597052,0.002558279],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004608651,0.00005272025,0.007799437,0.007364628,0.001627245,0.0008976613,0.006460709,0.001392873,0.0003738652,0.1657014,0.6944119,0.1134568],"study_design_scores_gemma":[0.0003432571,0.0001903631,0.02162434,0.0251224,0.001602793,0.001607567,0.003775134,0.001547304,0.001506243,0.09346278,0.8488985,0.0003193514],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002037857,0.08564556,0.009952541,0.8730117,0.02591014,0.0001735835,0.0007389204,0.00006004762,0.002469684],"genre_scores_gemma":[0.05879097,0.04534062,0.02623272,0.8087631,0.05728844,0.0007808675,0.0006354449,0.0001520306,0.002015658],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.630704,"threshold_uncertainty_score":0.7777703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289485109003761,"score_gpt":0.4533669741319672,"score_spread":0.3244184632315911,"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."}}