{"id":"W6945346711","doi":"10.25384/sage.c.6853854","title":"Marginal structural models with latent class growth analysis of treatment trajectories: Statins for primary prevention among older adults","year":2023,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier universitaire de Québec; University of Manitoba; Institute for Clinical Evaluative Sciences; Université de Sherbrooke; Women's College Hospital; University of Toronto; Université de Montréal; Université Laval","funders":"","keywords":"Marginal structural model; Covariate; Latent class model; Confounding; Proportional hazards model; Estimator; Latent variable; Confidence interval; Inverse probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01048319,0.0007401109,0.0009291534,0.001632729,0.0005365901,0.001050949,0.001794826,0.0006242784,0.002518454],"category_scores_gemma":[0.0269669,0.0002986293,0.001857158,0.001672866,0.001112314,0.001191677,0.001736128,0.001696574,0.0003322601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002230093,"about_ca_system_score_gemma":0.002445397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04727516,"about_ca_topic_score_gemma":0.04780172,"domain_scores_codex":[0.997043,0.002247714,0.00006667808,0.0002844474,0.0002301545,0.000128102],"domain_scores_gemma":[0.98874,0.008748109,0.000688087,0.0009778999,0.0006916034,0.0001542433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00075912,0.0002716384,0.1019292,0.0002494821,0.0007844389,0.0002697241,0.001645135,0.4484739,0.001452437,0.2150085,0.004627426,0.224529],"study_design_scores_gemma":[0.00002818531,0.00005129156,0.00667049,0.00002474436,0.00005554436,0.00003895947,0.0001188625,0.9344218,0.0002611083,0.05718366,0.001122254,0.00002316458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06647906,0.0002455274,0.9305111,0.000621817,0.00003097191,0.0002004211,0.0007675998,0.0003610354,0.0007823908],"genre_scores_gemma":[0.7165751,0.0004422843,0.2772108,0.0001362659,0.00005916749,0.0006718833,0.001844514,0.0001446164,0.002915265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04727516,"threshold_uncertainty_score":0.09399998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023426635237408,"score_gpt":0.3006640080476661,"score_spread":0.2804297416952921,"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."}}