{"id":"W4394782483","doi":"10.1093/biomet/asae021","title":"Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes","year":2024,"lang":"en","type":"article","venue":"Biometrika","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; York University","keywords":"Sensitivity (control systems); Mathematics; Binary number; Observational study; Matching (statistics); Statistics; Binary data; Population; Econometrics; Medicine","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":[0.0005899311,0.0001730972,0.0005118381,0.0008515581,0.00006762673,0.00006353741,0.0000491557,0.00006209459,0.000007598658],"category_scores_gemma":[0.001098849,0.0001183316,0.0001032831,0.001913579,0.0001234139,0.0001741076,0.00005760834,0.00006261899,0.000001418446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005483094,"about_ca_system_score_gemma":0.00002561141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001143581,"about_ca_topic_score_gemma":0.00004471,"domain_scores_codex":[0.9990579,0.00003958031,0.0002343603,0.0002918434,0.0002026918,0.0001736057],"domain_scores_gemma":[0.9958857,0.003598145,0.000074911,0.0001930338,0.0002077596,0.00004050427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002176456,0.0003771269,0.6796612,0.001989927,0.01533722,0.0002253166,0.002286046,0.00002556231,0.02455562,0.2492437,0.008470243,0.01761049],"study_design_scores_gemma":[0.001240983,0.001630873,0.5120906,0.0004517885,0.004709781,0.00004723079,0.00303645,0.002843029,0.03052879,0.4367779,0.005122071,0.001520504],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8157783,0.001532405,0.180751,0.000740909,0.00006140827,0.0004223968,0.0001279907,0.0005428195,0.00004278205],"genre_scores_gemma":[0.8525074,0.00006862682,0.1466942,0.00006176731,0.00002940084,0.00009232028,0.00002076436,0.00002086393,0.0005046334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1875343,"threshold_uncertainty_score":0.4825424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.345015430947697,"score_gpt":0.4661735827427737,"score_spread":0.1211581517950767,"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."}}