{"id":"W4385698578","doi":"10.1186/s12874-023-01995-5","title":"Genetic matching for time-dependent treatments: a longitudinal extension and simulation study","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Centre for Applied Research in Cancer Control","funders":"Terry Fox Research Institute; Health Innovation Network South London; Genome British Columbia; Genome Canada","keywords":"Covariate; Propensity score matching; Matching (statistics); Statistics; Confounding; Baseline (sea); Computer science; Mathematics; Econometrics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0153401,0.0001335748,0.0003328986,0.0001785142,0.0002625252,0.00001477604,0.0001923279,0.0003176089,0.00006640005],"category_scores_gemma":[0.03196527,0.0001116178,0.00007074881,0.0001969611,0.0001683377,0.000002491117,0.0003661667,0.0001824343,0.00004557591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002951393,"about_ca_system_score_gemma":0.0002448749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001705705,"about_ca_topic_score_gemma":0.0002687247,"domain_scores_codex":[0.9925459,0.00534979,0.0003722778,0.0006678082,0.0004706631,0.0005935424],"domain_scores_gemma":[0.9905929,0.00853255,0.00007051454,0.0003327322,0.0002332222,0.000238057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002074498,0.001323116,0.6658695,0.0002340088,0.0008966748,0.0001253303,0.001587682,0.02859096,0.1255705,0.0001644465,0.007901119,0.1656622],"study_design_scores_gemma":[0.002942221,0.003128663,0.9155143,0.00002218116,0.00006971101,0.00003692067,0.002001487,0.06872434,0.0001959567,0.006190401,0.0009344236,0.000239386],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8027096,0.000265174,0.1959535,0.0002709038,0.00009677001,0.0006518755,0.000007534667,0.00001959222,0.00002502759],"genre_scores_gemma":[0.9620461,0.0003340074,0.0356546,0.00008307027,0.0003434803,0.000290259,0.00007388636,0.00002697375,0.001147627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2496448,"threshold_uncertainty_score":0.9761889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3928024778635604,"score_gpt":0.5465209185638702,"score_spread":0.1537184407003098,"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."}}