{"id":"W2013383304","doi":"10.1177/1740774508093981","title":"Determining optimal sample sizes for multi-stage randomized clinical trials using value of information methods","year":2008,"lang":"en","type":"article","venue":"Clinical Trials","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Randomized controlled trial; Statistics; Sample size determination; Value (mathematics); Clinical trial; Stage (stratigraphy); Medicine; Computer science; Mathematics; Internal medicine; 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":[0.1331205,0.002678459,0.004059573,0.004306244,0.0007294276,0.00294825,0.003505925,0.004293827,0.005316422],"category_scores_gemma":[0.2630484,0.001381624,0.003698519,0.002856117,0.0033723,0.003495673,0.00293002,0.006092934,0.0008402718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004482172,"about_ca_system_score_gemma":0.006218032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009234291,"about_ca_topic_score_gemma":0.001041266,"domain_scores_codex":[0.8053177,0.1786574,0.003986257,0.003336788,0.007843776,0.000858063],"domain_scores_gemma":[0.6181996,0.3643983,0.008389111,0.004095166,0.004042701,0.0008751885],"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":[0.003425201,0.0006453918,0.00413061,0.005809814,0.001374816,0.0004663149,0.0007575793,0.2808238,0.001595647,0.3506906,0.005545977,0.3447343],"study_design_scores_gemma":[0.002561767,0.001446759,0.001350523,0.002178897,0.0005664905,0.0002682751,0.0001068157,0.4024191,0.002715689,0.578439,0.007785543,0.000161163],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001753982,0.001332452,0.9933969,0.0006921188,0.00007896277,0.001645841,0.00005730065,0.00008331632,0.0009591022],"genre_scores_gemma":[0.06866167,0.001435869,0.9215386,0.0004574713,0.0001274209,0.007205859,0.00008768585,0.00006515097,0.0004203123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8668795,"threshold_uncertainty_score":0.7040167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9543317968023223,"score_gpt":0.7614272996126612,"score_spread":0.1929044971896611,"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."}}