{"id":"W2972625246","doi":"10.1016/j.conctc.2019.100446","title":"Minimizing control group allocation in randomized trials using dynamic borrowing of external control data – An application to second line therapy for non-small cell lung cancer","year":2019,"lang":"en","type":"article","venue":"Contemporary Clinical Trials Communications","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; McMaster University; Impact","funders":"","keywords":"Lung cancer; Control (management); Randomized controlled trial; Medicine; Oncology; Computer science; Internal medicine; Artificial intelligence","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":[0.08678116,0.001217607,0.001265184,0.001271887,0.0005295388,0.001458336,0.002395785,0.00142807,0.002396813],"category_scores_gemma":[0.1893592,0.0007524847,0.00231844,0.001298025,0.003072641,0.001818147,0.002812462,0.002242204,0.0001664017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002461979,"about_ca_system_score_gemma":0.002780875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002179001,"about_ca_topic_score_gemma":0.002327662,"domain_scores_codex":[0.9259132,0.06790137,0.001164288,0.002143849,0.002439506,0.0004378966],"domain_scores_gemma":[0.7800407,0.1951391,0.01208659,0.01005568,0.001888215,0.0007896627],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001528882,0.0003072659,0.008866521,0.0007189299,0.0009781297,0.0002775191,0.000990807,0.6679328,0.002253721,0.1249146,0.001392201,0.1898386],"study_design_scores_gemma":[0.0005435476,0.0009990571,0.003662834,0.0002596393,0.0002920281,0.0001332613,0.0001014048,0.8049722,0.003349161,0.1806597,0.004927185,0.0001000446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03714619,0.0005475156,0.9591748,0.0007960111,0.00003443952,0.0007059488,0.0001423496,0.0001521712,0.001300547],"genre_scores_gemma":[0.575229,0.0003448059,0.4211046,0.0005406237,0.00004459546,0.001560624,0.0002199713,0.00007862342,0.000877113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9132189,"threshold_uncertainty_score":0.4589481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7204909650107075,"score_gpt":0.6257871096498172,"score_spread":0.09470385536089032,"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."}}