{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01440223,0.0007511929,0.00324536,0.002342283,0.0003147868,0.002497314,0.003144553,0.00224976,0.002138493],"category_scores_gemma":[0.01832125,0.0004063073,0.00177125,0.002532731,0.001420981,0.002258949,0.001477056,0.004803494,0.001270615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003129449,"about_ca_system_score_gemma":0.007020902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004168627,"about_ca_topic_score_gemma":0.006136903,"domain_scores_codex":[0.996904,0.001085889,0.0005585916,0.0004813391,0.000785129,0.0001850486],"domain_scores_gemma":[0.9865725,0.00797847,0.001411529,0.000480838,0.003276624,0.0002801128],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002376875,0.00004493999,0.0009327983,0.01258668,0.0004123657,0.00009213766,0.00016484,0.0008164933,0.0004052078,0.01054023,0.03176975,0.9419968],"study_design_scores_gemma":[0.0001196138,0.0002650619,0.004493691,0.0163071,0.001047998,0.001455427,0.0001644263,0.0004345637,0.0009891865,0.008757554,0.9658784,0.0000869576],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006152365,0.9896011,0.0009580219,0.005928965,0.0005689171,0.00002497154,0.0001056894,0.00002933644,0.002167724],"genre_scores_gemma":[0.01006227,0.9759898,0.004763314,0.006220605,0.001317602,0.000100382,0.0005420828,0.00006684876,0.0009370059],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9855978,"threshold_uncertainty_score":0.07616717,"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."}}