{"id":"W4409907352","doi":"10.1016/j.cjca.2025.04.020","title":"Compared to Whom? Using Target Trial Emulation to Improve Causal Inference","year":2025,"lang":"en","type":"letter","venue":"Canadian Journal of Cardiology","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Fonds de Recherche du Québec - Santé","keywords":"Medicine; Emulation; Causal inference; Inference; Artificial intelligence; Social psychology; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008884791,0.000325095,0.0009232371,0.0005180077,0.0002468417,0.00007309869,0.0005401233,0.0005768559,0.0003497522],"category_scores_gemma":[0.0007902359,0.000344877,0.0002018596,0.0002956882,0.0002072084,0.000140746,0.0001122674,0.001546165,0.0001728355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003199817,"about_ca_system_score_gemma":0.002059815,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009656196,"about_ca_topic_score_gemma":0.003786863,"domain_scores_codex":[0.9968259,0.0006277086,0.0007683201,0.0005472787,0.0003522557,0.0008785042],"domain_scores_gemma":[0.9980081,0.0002670698,0.0003700377,0.000403862,0.00005727827,0.0008936115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003954047,0.000001780765,0.01039524,0.00002700953,0.0001429334,0.0009296276,0.0004417838,0.1557846,0.0006018104,0.000002907785,0.8271914,0.004085498],"study_design_scores_gemma":[0.002191996,0.0003074122,0.009018379,0.00009086455,0.00009058661,0.00005706585,0.00003097892,0.0002138748,0.00003447319,0.0001587427,0.9874505,0.0003551255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3024095,0.000124396,0.2076431,0.4529292,0.01507978,0.004664863,0.0008782652,0.00002932791,0.01624157],"genre_scores_gemma":[0.6060707,0.000006491511,0.003459852,0.3805036,0.00922009,0.00002741948,0.00006613517,0.00006052702,0.000585188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3036612,"threshold_uncertainty_score":0.9999003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410085223247999,"score_gpt":0.2911997822426823,"score_spread":0.2570989300102023,"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."}}