{"id":"W4402716952","doi":"10.1101/2024.09.19.24313989","title":"RaBIt: An Effective Visualization-Driven Tool for Power and Sample Size Estimation in Two-Stage General Randomized Basket Trial Designs","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Sports Dynamics and Biomechanics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Hospital for Sick Children; Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"University of Toronto","keywords":"Sample size determination; Stage (stratigraphy); Visualization; Computer science; Randomized controlled trial; Sample (material); Estimation; Statistics; Mathematics; Data mining; Engineering; Physics; Biology; Systems engineering; Medicine; Thermodynamics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001243048,0.0003282692,0.0007077729,0.0002255713,0.00003519755,0.0001558353,0.0001247212,0.0002287325,0.00004472717],"category_scores_gemma":[0.0007548183,0.000302263,0.0001775923,0.0001512298,0.00002844379,0.00006588582,0.0001212672,0.0002637176,0.00000165395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001320379,"about_ca_system_score_gemma":0.00004357961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007227173,"about_ca_topic_score_gemma":0.00009139107,"domain_scores_codex":[0.9985118,0.0001310734,0.0005125578,0.0004498045,0.0001612317,0.0002335461],"domain_scores_gemma":[0.9984707,0.001065353,0.0000924715,0.000253386,0.00005455664,0.00006359182],"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":[0.1219871,0.0002717201,0.001128764,0.0024369,0.0008527995,0.00005042769,0.002865983,0.8268599,0.01005469,0.02696753,0.00008368163,0.006440552],"study_design_scores_gemma":[0.09076983,0.00005485164,0.0002426348,0.00009231464,0.00009997808,8.520979e-7,0.00000803674,0.8902864,0.0002919914,0.01780587,0.00002694926,0.000320283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7004611,0.00007076985,0.2945172,0.00001199124,0.001310343,0.003236707,0.0002140064,0.0001607103,0.00001710013],"genre_scores_gemma":[0.974279,0.00005333786,0.02374631,0.00001721602,0.0001663779,0.001299459,0.0002992656,0.00009635319,0.00004272311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2738178,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335764782126574,"score_gpt":0.2971150325339259,"score_spread":0.2837573847126602,"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."}}