{"id":"W2519427156","doi":"10.1177/2168479016665434","title":"DIA’s Adaptive Design Scientific Working Group (ADSWG): Best Practices Case Studies for “Less Well-understood” Adaptive Designs","year":2016,"lang":"en","type":"article","venue":"Therapeutic Innovation & Regulatory Science","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"inVentiv Health Clinical","funders":"","keywords":"Toolbox; Clinical study design; Adaptive design; Computer science; Clinical trial; Research design; Risk analysis (engineering); Management science; Pharmacy; Data science; Medicine; Engineering; Family medicine","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":[0.438512,0.001952567,0.002118593,0.004328439,0.003355339,0.01177106,0.01024405,0.02060683,0.01778151],"category_scores_gemma":[0.6733547,0.002126933,0.003320235,0.003156989,0.01249236,0.01048131,0.01205866,0.02350256,0.005302856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006642695,"about_ca_system_score_gemma":0.03389415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00183659,"about_ca_topic_score_gemma":0.002616207,"domain_scores_codex":[0.4844773,0.4134729,0.03048511,0.008853115,0.05985307,0.00285858],"domain_scores_gemma":[0.1486908,0.6909634,0.0163609,0.07085809,0.0651191,0.008007698],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001165322,0.0008135866,0.002628031,0.003287264,0.0004257,0.000381778,0.003471073,0.005830631,0.002039001,0.3603641,0.2296592,0.3899344],"study_design_scores_gemma":[0.001406302,0.0007884352,0.001182798,0.006670945,0.0003197174,0.0006653869,0.000624318,0.01731876,0.004952048,0.5933098,0.372528,0.0002335108],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002663942,0.004565518,0.7678468,0.1964231,0.006519906,0.00258891,0.0004120196,0.001452648,0.01752721],"genre_scores_gemma":[0.02510692,0.00170957,0.9220983,0.03866464,0.002163756,0.005307849,0.0003027229,0.0008373533,0.003808915],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.561488,"threshold_uncertainty_score":0.6924146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8818243861039051,"score_gpt":0.5764153350103045,"score_spread":0.3054090510936006,"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."}}