{"id":"W4390690845","doi":"10.21203/rs.3.rs-3829888/v1","title":"Part II: Adaptive designs in pediatric clinical trials: specific examples, comparison with adult trials and a discussion for the child health community","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; George & Fay Yee Centre for Healthcare Innovation; University of Toronto; Robarts Clinical Trials; University of Calgary; University of Manitoba","funders":"Canadian Institutes of Health Research; Seqirus; Research Manitoba; GlaxoSmithKline","keywords":"Clinical trial; Frequentist inference; Medicine; Clinical study design; Medical physics; Research design; Adaptive design; Randomization; Bayesian probability; Computer science; Artificial intelligence; Bayesian inference; Pathology; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1518482,0.001594893,0.002385647,0.002382386,0.000591824,0.004806594,0.003244088,0.005824886,0.009565867],"category_scores_gemma":[0.2850142,0.0008011482,0.002501174,0.004605435,0.007245241,0.004561573,0.002317208,0.006776215,0.002490253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003173474,"about_ca_system_score_gemma":0.004473597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001607107,"about_ca_topic_score_gemma":0.001889583,"domain_scores_codex":[0.8507577,0.1365187,0.004420193,0.001930792,0.005859022,0.0005135796],"domain_scores_gemma":[0.66627,0.3065511,0.006854285,0.01211358,0.007273869,0.0009371176],"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":[0.0005909714,0.000167726,0.001600494,0.006944467,0.0006666541,0.000171786,0.0007386167,0.00495358,0.0008816178,0.6477146,0.1105198,0.2250497],"study_design_scores_gemma":[0.0005994733,0.0007664185,0.004211975,0.007423681,0.0004630266,0.0008439622,0.0003357254,0.01053736,0.001638238,0.6826059,0.2904674,0.0001068341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002994396,0.1796865,0.7180382,0.07181395,0.007209179,0.001873631,0.0007890849,0.0005389946,0.017056],"genre_scores_gemma":[0.04249931,0.08460991,0.7998103,0.03942966,0.01244943,0.009343584,0.0004975338,0.0008598507,0.01050032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8481518,"threshold_uncertainty_score":0.8030596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9446607035116983,"score_gpt":0.7205767458541128,"score_spread":0.2240839576575855,"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."}}