{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1226473,0.0004269152,0.007768412,0.001509783,0.00003562956,0.00001477229,0.0007643407,0.0006518047,0.004294911],"category_scores_gemma":[0.7008931,0.0003739271,0.0004350162,0.001826213,0.0006704637,0.00002054328,0.0006515215,0.002806242,0.000001037265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002883217,"about_ca_system_score_gemma":0.0006250569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006187393,"about_ca_topic_score_gemma":0.0005772582,"domain_scores_codex":[0.9569511,0.02266446,0.01676714,0.001315416,0.001818828,0.0004829932],"domain_scores_gemma":[0.7335626,0.2595199,0.00511433,0.001343402,0.0002916185,0.0001682118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009830398,0.001642991,0.05772521,0.002152183,0.003527104,0.0002344669,0.001525021,0.009093306,0.00008156372,0.8835959,0.00146696,0.03797219],"study_design_scores_gemma":[0.001919992,0.0001862567,0.02737562,0.0004769447,0.004826011,4.153996e-7,0.000337105,0.0903461,0.000006251219,0.8742023,0.00004852608,0.0002745001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03029392,0.0001517533,0.9579756,0.0002059761,0.003484231,0.002005923,0.004324238,0.00003244609,0.001525916],"genre_scores_gemma":[0.05305231,0.0003379475,0.9455417,0.0000650581,0.0004922894,0.0001171703,0.0002783676,0.00005282253,0.00006227599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5782458,"threshold_uncertainty_score":0.9998713,"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."}}