{"id":"W4283268556","doi":"10.1002/pds.5500","title":"Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature","year":2022,"lang":"en","type":"review","venue":"Pharmacoepidemiology and Drug Safety","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Agency for Drugs and Technologies in Health; McGill University; McGill University Health Centre","funders":"National Institute on Aging; International Society for Pharmacoepidemiology","keywords":"Confounding; Proxy (statistics); Prioritization; Covariate; Medicine; Feature selection; Computer science; Data mining; Machine learning; Management science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05730677,0.00127893,0.002823567,0.00459539,0.0005573719,0.003705651,0.002256124,0.001517055,0.002116807],"category_scores_gemma":[0.1462988,0.0007478427,0.004087377,0.005723337,0.001201311,0.002466677,0.001985134,0.003897265,0.0005860718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00168208,"about_ca_system_score_gemma":0.002872703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00285288,"about_ca_topic_score_gemma":0.002777143,"domain_scores_codex":[0.9625069,0.02884924,0.003334505,0.002224559,0.002851287,0.0002335511],"domain_scores_gemma":[0.8355105,0.1487227,0.006202491,0.00383753,0.005244078,0.0004826678],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003259007,0.0001488463,0.02605864,0.0112038,0.003395775,0.00008927385,0.0002992446,0.01831806,0.0003646395,0.01805735,0.01228943,0.909449],"study_design_scores_gemma":[0.0007617003,0.001008542,0.06077116,0.03490771,0.006261589,0.001185043,0.0005656169,0.4703289,0.003245625,0.2695059,0.1509458,0.000512365],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007869272,0.4519698,0.5159632,0.0188843,0.0008194709,0.0005688662,0.001281655,0.0006798458,0.001963576],"genre_scores_gemma":[0.1574282,0.2499934,0.5777544,0.006027073,0.003906363,0.001836423,0.002167721,0.0002221446,0.0006642368],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9426932,"threshold_uncertainty_score":0.3030708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4355611869380462,"score_gpt":0.5536874237850973,"score_spread":0.118126236847051,"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."}}