{"id":"W4389899943","doi":"10.1111/jebm.12577","title":"Reporting, handling, and interpretation of time‐varying drug treatments in observational studies using routinely collected healthcare data","year":2023,"lang":"en","type":"article","venue":"Journal of Evidence-Based Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"National Natural Science Foundation of China","keywords":"Observational study; Discontinuation; Medicine; Logistic regression; Protocol (science); Pharmacoepidemiology; Meta-analysis; Internal medicine; Alternative medicine; Pharmacology; Pathology; Medical prescription","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"reporting","study_design":"systematic_review","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"reporting","study_design":"observational","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004663147,0.0001517675,0.000810675,0.0004986415,0.00006006685,0.000007993114,0.000196494,0.00004939621,0.00001113314],"category_scores_gemma":[0.04846497,0.0001017685,0.00003893546,0.0007609215,0.0001345955,0.0005696526,0.00009086275,0.0002159604,2.372236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002423251,"about_ca_system_score_gemma":0.0004187647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093345,"about_ca_topic_score_gemma":0.00003988371,"domain_scores_codex":[0.9964725,0.0001961088,0.002417164,0.0001917723,0.0005550134,0.0001674161],"domain_scores_gemma":[0.9908666,0.003566196,0.004308049,0.0002833054,0.0009046498,0.00007117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004199823,0.0007701248,0.733161,0.009010569,0.001600936,0.001811934,0.03513645,0.008638682,0.1444415,0.001136397,0.02684068,0.03325182],"study_design_scores_gemma":[0.008592253,0.004225258,0.06023731,0.2047412,0.00155526,0.0004061743,0.007079408,0.5259057,0.0260165,0.1603393,0.00006128583,0.0008403771],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845934,0.003034509,0.007225073,0.004636531,0.0001223043,0.0003218699,0.000008493677,0.00004850972,0.000009329142],"genre_scores_gemma":[0.9517133,0.001301346,0.04658158,0.0001553755,0.0001170726,0.000003964426,0.00002013645,0.00001977054,0.00008745844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6729237,"threshold_uncertainty_score":0.9595502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8265753926692315,"score_gpt":0.5867571796628459,"score_spread":0.2398182130063856,"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."}}