{"id":"W2067158811","doi":"10.1097/ede.0b013e3181e00730","title":"Marginalia","year":2010,"lang":"fr","type":"letter","venue":"Epidemiology","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Minority Health and Health Disparities","keywords":"Marginal structural model; Covariate; Inverse probability; Marginal distribution; Econometrics; Statistics; Inverse probability weighting; Mathematics; Confounding; Contrast (vision); Marginal model; Weighting; Computer science; Posterior probability; Propensity score matching; Regression analysis; Medicine; Bayesian probability; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005015307,0.001900246,0.001512259,0.003125951,0.003459118,0.009243474,0.003632247,0.002636769,0.1790444],"category_scores_gemma":[0.02295173,0.0008883783,0.002609657,0.003450019,0.00545562,0.0101725,0.007254253,0.0043838,0.08502839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003796244,"about_ca_system_score_gemma":0.003498106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004137604,"about_ca_topic_score_gemma":0.003237379,"domain_scores_codex":[0.9910357,0.002635017,0.0007544716,0.002334855,0.002244384,0.0009955309],"domain_scores_gemma":[0.9929776,0.002014331,0.0005713177,0.001999976,0.002120261,0.0003166329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006291149,0.00002817091,0.0006705324,0.0003162814,0.00004649898,0.0001789386,0.0006381725,0.0005165371,0.0002279972,0.8787679,0.05819229,0.06035377],"study_design_scores_gemma":[0.00001607912,0.00002245023,0.0003539911,0.0002720959,0.00003052245,0.0005774298,0.0002652635,0.001088316,0.0003705061,0.3689464,0.6280313,0.00002568797],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.00582316,0.009395658,0.268942,0.01049254,0.004530048,0.0006067093,0.007195922,0.002982307,0.6900316],"genre_scores_gemma":[0.2309897,0.01998633,0.210406,0.01305517,0.005377193,0.002668003,0.01706422,0.00463742,0.495816],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1790444,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3183756489211864,"score_gpt":0.4717226469922707,"score_spread":0.1533469980710843,"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."}}