{"id":"W2752545986","doi":"10.1002/sim.7453","title":"Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements","year":2017,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Medical Research Council Canada; Cancer Research UK; AstraZeneca","keywords":"Response Evaluation Criteria in Solid Tumors; Sample size determination; Clinical endpoint; Clinical trial; Estimator; Medicine; Oncology; Outcome (game theory); Complete response; Computer science; Statistics; Internal medicine; Phases of clinical research; Mathematics; Chemotherapy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.09093987,0.001648137,0.002979416,0.001298041,0.0004859234,0.002448928,0.001979996,0.00242141,0.002217812],"category_scores_gemma":[0.2243156,0.0009461241,0.002419698,0.001597226,0.001186751,0.002479433,0.00216064,0.003072035,0.0006209547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008417588,"about_ca_system_score_gemma":0.002422697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007358298,"about_ca_topic_score_gemma":0.0007639331,"domain_scores_codex":[0.8937006,0.09705132,0.002419661,0.002338897,0.004063839,0.0004256911],"domain_scores_gemma":[0.7663972,0.2032295,0.01382148,0.01142972,0.004280506,0.0008415512],"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":[0.007315574,0.0006309902,0.01645381,0.002925557,0.003069601,0.0002981735,0.0006654452,0.5012247,0.005192518,0.04859205,0.007423808,0.4062078],"study_design_scores_gemma":[0.001918906,0.002335176,0.004118826,0.0003060665,0.0005992546,0.0002705618,0.00004425861,0.9092786,0.003072994,0.06867177,0.009247239,0.000136424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01361829,0.001483434,0.9814689,0.001040692,0.0001789002,0.0006709261,0.0001813609,0.0004194764,0.0009381004],"genre_scores_gemma":[0.3146806,0.0008581184,0.6793344,0.0008046667,0.0002349016,0.002401374,0.0005046632,0.0001990473,0.0009822878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9090601,"threshold_uncertainty_score":0.4809418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8504567930226415,"score_gpt":0.6474845982302354,"score_spread":0.2029721947924061,"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."}}