{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01492523,0.0002023263,0.002106454,0.0001322987,0.00007531133,0.00001332012,0.000259759,0.0005489259,0.00007560469],"category_scores_gemma":[0.01703432,0.0001496624,0.001277446,0.0002047523,0.0002979989,0.000009540224,0.0001346741,0.0005039506,0.0000152442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009780611,"about_ca_system_score_gemma":0.001798488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000307573,"about_ca_topic_score_gemma":0.0003770019,"domain_scores_codex":[0.9931652,0.001840263,0.0040093,0.0003666857,0.0002399685,0.0003786043],"domain_scores_gemma":[0.9897368,0.007308408,0.001963974,0.0002354787,0.000492718,0.0002626107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.03044456,0.00268052,0.1188145,0.0000256802,0.002283625,0.0002130464,0.0001054488,0.0001198206,0.0005766455,0.000200915,0.06621594,0.7783192],"study_design_scores_gemma":[0.04480111,0.004984844,0.8047025,0.0001373152,0.0006323915,0.0001623978,0.000245596,0.00003531685,0.0002487106,0.001620702,0.1421566,0.0002725809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552195,0.001718207,0.0007964339,0.03322123,0.006929833,0.0006986707,0.0009162311,0.00002071552,0.0004791964],"genre_scores_gemma":[0.975755,0.01452149,0.002683674,0.001856384,0.004864534,0.00009165715,0.00006894232,0.00004357112,0.0001147334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7780467,"threshold_uncertainty_score":0.9912456,"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."}}