{"id":"W4200631872","doi":"10.1002/sim.9439","title":"Using principal stratification in analysis of clinical trials","year":2022,"lang":"en","type":"preprint","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bayer (Canada)","funders":"","keywords":"Principal (computer security); Computer science; Variety (cybernetics); Observational study; Set (abstract data type); Addendum; Key (lock); Data science; Stratification (seeds); Management science; Machine learning; Risk analysis (engineering); Artificial intelligence; Mathematics; Engineering; Statistics; Medicine; Programming language; Biology","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":[0.1337323,0.002337266,0.003384431,0.00635017,0.001349993,0.006358248,0.00221631,0.002809778,0.005964548],"category_scores_gemma":[0.3669349,0.001956472,0.004929899,0.006031215,0.00596223,0.005829196,0.006144235,0.007146493,0.002327172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003252199,"about_ca_system_score_gemma":0.008804154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002668715,"about_ca_topic_score_gemma":0.001953396,"domain_scores_codex":[0.7908968,0.1862816,0.00660211,0.006700332,0.008401618,0.001117548],"domain_scores_gemma":[0.6662491,0.2838002,0.01366132,0.02740992,0.007608581,0.001270911],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006494215,0.000079281,0.007251163,0.002254416,0.002098619,0.0002293548,0.001219755,0.05179583,0.0005462824,0.6771648,0.01605831,0.2406528],"study_design_scores_gemma":[0.0002222882,0.0001504975,0.0008525815,0.0003980553,0.0001850503,0.00007265602,0.00005268058,0.07685173,0.0005610946,0.908865,0.01171571,0.0000725303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001157159,0.001141526,0.9941268,0.001185842,0.0002553212,0.0003612194,0.0002630098,0.000580923,0.0009281858],"genre_scores_gemma":[0.06633434,0.00226342,0.9234884,0.001072322,0.0009232778,0.003936307,0.0007278374,0.0004023181,0.0008517635],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8662677,"threshold_uncertainty_score":0.7072523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9143698492728977,"score_gpt":0.7492055652700645,"score_spread":0.1651642840028331,"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."}}