{"id":"W4306399408","doi":"10.31219/osf.io/p6c5v","title":"Navigating analytical challenges in clinical trials using the multiverse approach","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"U.S. Army Medical Department; U.S. Department of Veterans Affairs; Defense Health Agency; U.S. Department of Defense","keywords":"Transparency (behavior); Clinical trial; Computer science; Data science; A priori and a posteriori; Psychology; Medicine; Epistemology; Computer security","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":["metaresearch"],"category_scores_codex":[0.6417242,0.005457799,0.01446178,0.01985937,0.003591507,0.01675314,0.008341606,0.008112594,0.005930771],"category_scores_gemma":[0.7422994,0.005357299,0.01569957,0.01085595,0.01230753,0.01161889,0.01497533,0.01826013,0.001012236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01035606,"about_ca_system_score_gemma":0.02041394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005142103,"about_ca_topic_score_gemma":0.004933676,"domain_scores_codex":[0.1935749,0.7459382,0.0221141,0.01547851,0.0217708,0.001123546],"domain_scores_gemma":[0.118667,0.8355458,0.01379529,0.02350117,0.007460725,0.001030004],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004388397,0.0004495589,0.0121846,0.02809197,0.06803477,0.002260703,0.008996087,0.06909591,0.001780851,0.5103911,0.0187863,0.2755398],"study_design_scores_gemma":[0.0009162042,0.0003978495,0.001050779,0.003040427,0.00433102,0.0003589653,0.0006263066,0.05016652,0.0009140726,0.9230861,0.0148419,0.0002698035],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003923927,0.01392852,0.9548873,0.01798112,0.001367692,0.0044389,0.0007179454,0.0006002269,0.002154365],"genre_scores_gemma":[0.07130169,0.003214349,0.9029562,0.00617455,0.001138915,0.01403465,0.000335858,0.0003228516,0.0005208696],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3582758,"threshold_uncertainty_score":0.4418179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9845613268298931,"score_gpt":0.7237888390298555,"score_spread":0.2607724878000376,"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."}}