{"id":"W4399630953","doi":"10.1287/msom.2023.0246","title":"Adaptive Seamless Dose-Finding Trials","year":2024,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Leverage (statistics); Computer science; Matching (statistics); Range (aeronautics); Toxicity; Mathematical optimization; Mathematics; Machine learning; Medicine; Statistics","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":[0.02265516,0.002364984,0.004043007,0.0009346384,0.0007151976,0.003144997,0.006575454,0.005482511,0.01088959],"category_scores_gemma":[0.05778005,0.001639672,0.001996939,0.001910258,0.002932791,0.004274311,0.002653788,0.005464478,0.00135435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002555998,"about_ca_system_score_gemma":0.003747506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002027849,"about_ca_topic_score_gemma":0.001141092,"domain_scores_codex":[0.9802346,0.01399911,0.0006200343,0.003513501,0.0008827011,0.0007501786],"domain_scores_gemma":[0.9380476,0.04990949,0.005154262,0.003315001,0.001583269,0.001990247],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001596449,0.0006347314,0.002740014,0.0007485932,0.0002378889,0.0003837945,0.0002104315,0.870617,0.0008381216,0.05023718,0.004201174,0.06755466],"study_design_scores_gemma":[0.0008834326,0.0006755578,0.0006254995,0.00006985399,0.0001001693,0.0001711513,0.00004624258,0.9303165,0.0007425374,0.06347953,0.002846806,0.00004273351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03429648,0.001622497,0.9520709,0.003164259,0.0002106526,0.002277456,0.0006059535,0.0005838317,0.005167961],"genre_scores_gemma":[0.6520764,0.0009655025,0.334575,0.001416815,0.0003584177,0.0033583,0.0006029584,0.0001355091,0.006511035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9773449,"threshold_uncertainty_score":0.1198134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6033093360047029,"score_gpt":0.5470555565121502,"score_spread":0.05625377949255272,"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."}}