{"id":"W939145883","doi":"10.1093/biostatistics/kxw049","title":"Inequality in treatment benefits: Can we determine if a new treatment benefits the many or the few?","year":2016,"lang":"en","type":"article","venue":"Biostatistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Patient-Centered Outcomes Research Institute; U.S. Food and Drug Administration; National Institute on Aging; National Institutes of Health; Hamilton Health Sciences Foundation","keywords":"Randomized controlled trial; Estimator; Fraction (chemistry); Computer science; Statistics; Econometrics; Randomized response; Outcome (game theory); Mathematics; Medicine; Mathematical optimization; Mathematical economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000214979,0.0004007481,0.0004138493,0.00006667107,0.0001392935,0.0000485306,0.0003367215,0.0001094389,0.0001329771],"category_scores_gemma":[0.000755597,0.0001390306,0.00007307849,0.0002151426,0.0001429006,0.00006763846,0.00009097594,0.00008357588,0.00002558547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007889955,"about_ca_system_score_gemma":0.0002329595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009943891,"about_ca_topic_score_gemma":0.01235679,"domain_scores_codex":[0.9982081,0.0001463597,0.0005736999,0.0003497249,0.0002588545,0.000463231],"domain_scores_gemma":[0.9957225,0.002893072,0.0002585994,0.000920674,0.00007724101,0.0001279431],"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":[0.0001232969,0.0003348755,0.001300696,0.0000245722,0.00007214468,0.00004955851,0.001969274,0.000006724904,0.0002114516,0.2163973,0.001642969,0.7778672],"study_design_scores_gemma":[0.006045599,0.005686139,0.01372785,0.001101701,0.0005380912,0.0001240198,0.00129407,0.0005119481,0.0339375,0.8852222,0.05030303,0.001507835],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5996963,0.002769901,0.2891898,0.07818333,0.001080264,0.01196212,0.0135077,0.001963117,0.001647484],"genre_scores_gemma":[0.8904461,0.006849898,0.09357225,0.0004888586,0.0003278439,0.0005925299,0.00003909538,0.0001304675,0.007553008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7763593,"threshold_uncertainty_score":0.6895381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2502387195907997,"score_gpt":0.4054696846381312,"score_spread":0.1552309650473315,"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."}}