{"id":"W4412493510","doi":"10.1038/s41746-025-01821-w","title":"Negative control-calibrated difference-in-difference analyses: addressing unmeasured confounding in RWD with application to racial/ethnic differences","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; National Eye Institute; National Institute of Mental Health; U.S. National Library of Medicine; Patient-Centered Outcomes Research Institute; National Institute on Aging; National Institutes of Health","keywords":"Confounding; Ethnic group; Causal inference; Health records; Health equity; Control (management); Health care; Medicine; Psychology; Computer science; Statistics; Mathematics; Public health; Artificial intelligence; Nursing; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003668761,0.000468269,0.001136297,0.0007723429,0.00007772416,0.000105005,0.0004674846,0.0001827962,0.00002501368],"category_scores_gemma":[0.003077459,0.000335327,0.0000461766,0.002174338,0.0004006728,0.0003949225,0.00008882007,0.000492643,0.000004918566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003272925,"about_ca_system_score_gemma":0.0001673934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001870038,"about_ca_topic_score_gemma":0.001058346,"domain_scores_codex":[0.9971445,0.0001513623,0.0009116132,0.0006749568,0.0005649578,0.0005526217],"domain_scores_gemma":[0.9966749,0.002068397,0.0003250584,0.0005109258,0.0002463601,0.0001743679],"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":[0.003513048,0.00169643,0.2520967,0.001202514,0.0005488937,0.0001956174,0.01558758,0.0003609693,0.2393036,0.2040203,0.0002641098,0.2812103],"study_design_scores_gemma":[0.006953993,0.00124723,0.3124357,0.01134569,0.0002033545,0.000009511136,0.004023012,0.007431225,0.01215485,0.6429501,0.0000393443,0.001205939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5851677,0.0001478976,0.4058101,0.000751872,0.0000646813,0.001377242,0.00001798151,0.0003094015,0.006353071],"genre_scores_gemma":[0.9964132,0.00001948768,0.002373483,0.000369863,0.00005131174,0.0004064519,0.00002046246,0.00003217834,0.0003136145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4389298,"threshold_uncertainty_score":0.9999099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.191599172895229,"score_gpt":0.4660973734039519,"score_spread":0.2744982005087229,"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."}}