{"id":"W4383896730","doi":"10.1200/jco.23.00435","title":"Standardized Definitions for Efficacy End Points in Neoadjuvant Breast Cancer Clinical Trials: NeoSTEEP","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Genentech; Bavarian Nordic; Siemens Healthineers; Eisai; National Center for Advancing Translational Sciences; Seagen; Puma Biotechnology; Advanced Accelerator Applications; Gilead Sciences; Daiichi Sankyo Europe; Exelixis; Sanofi; Astellas Pharma; Mylan; GlaxoSmithKline; Pfizer; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb","keywords":"Medicine; Breast cancer; Clinical trial; Neoadjuvant therapy; Clinical endpoint; Oncology; Cancer; Surrogate endpoint; Internal medicine; Medical physics; Surgery","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":[0.2658592,0.002612656,0.003928341,0.007076143,0.001426438,0.006748146,0.004645068,0.003849254,0.004016013],"category_scores_gemma":[0.2936791,0.001119992,0.008782026,0.006373262,0.003529474,0.004165714,0.008408066,0.009326326,0.002857421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005246016,"about_ca_system_score_gemma":0.01852239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009006294,"about_ca_topic_score_gemma":0.0009899404,"domain_scores_codex":[0.6485953,0.260247,0.0594285,0.004157934,0.02482302,0.002748149],"domain_scores_gemma":[0.6185529,0.2039565,0.06309635,0.03047796,0.0780588,0.00585748],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01722188,0.0006806838,0.0201974,0.03734689,0.005615756,0.000530861,0.001761867,0.01785197,0.002294967,0.2023439,0.1893725,0.5047814],"study_design_scores_gemma":[0.009447084,0.006133603,0.03165082,0.0438296,0.00417826,0.001456309,0.0007414091,0.01658564,0.005305721,0.1641469,0.7159699,0.0005548165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01982504,0.08620553,0.6394057,0.02902895,0.00960656,0.1175294,0.02796299,0.002426424,0.06800934],"genre_scores_gemma":[0.0838104,0.01446714,0.5675706,0.01977833,0.003067846,0.2772697,0.02812683,0.0009045267,0.005004634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7341408,"threshold_uncertainty_score":0.9053263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3488729579279656,"score_gpt":0.5602541676005899,"score_spread":0.2113812096726243,"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."}}