{"id":"W2397768676","doi":"10.1016/j.ejca.2016.03.082","title":"RECIST 1.1 – Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group","year":2016,"lang":"en","type":"review","venue":"European Journal of Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":380,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Cancer Institute; Canadian Cancer Society Research Institute","keywords":"Response Evaluation Criteria in Solid Tumors; Medicine; Clinical trial; Medical physics; Clinical endpoint; Biomarker; Imaging biomarker; Radiology; Phases of clinical research; Internal medicine; Magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001420628,0.0002292712,0.0007565885,0.0001060552,0.0001643066,0.0001162303,0.00021529,0.00002865951,0.0002050424],"category_scores_gemma":[0.0009367942,0.0001161779,0.0003312728,0.0001323688,0.0002701978,0.00007848246,0.00004721131,0.000811192,0.00001037496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002700333,"about_ca_system_score_gemma":0.0002972714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007465061,"about_ca_topic_score_gemma":0.000001709701,"domain_scores_codex":[0.9977061,0.000727528,0.0006782851,0.0002458243,0.0004894435,0.0001528086],"domain_scores_gemma":[0.9978621,0.0005512132,0.0008705794,0.0002462193,0.000178735,0.0002911403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008264116,0.00001369294,0.00005741397,0.0001691543,0.0002473563,0.0001867851,0.0006172766,9.687158e-7,0.000001440553,0.0001504748,0.009310725,0.9891621],"study_design_scores_gemma":[0.0005949429,0.00005123229,0.00110415,0.0387699,0.0007598386,0.00007710238,0.0002681348,0.000005460375,1.984049e-8,0.00007756668,0.9581641,0.000127531],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000024684,0.9887686,0.00406142,0.003787645,0.0007828773,0.0002087302,0.0000430011,0.0000120083,0.002311069],"genre_scores_gemma":[0.0003955052,0.9929895,0.0009496615,0.0001807033,0.004794102,0.000004150248,0.00001260764,0.00007824313,0.0005955435],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9890345,"threshold_uncertainty_score":0.4737596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05869387304731272,"score_gpt":0.3462172372383782,"score_spread":0.2875233641910655,"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."}}