{"id":"W3111804892","doi":"10.1093/neuonc/noaa222.346","title":"IMG-10. MRI-BASED RADIOMIC PROGNOSTIC MARKERS OF DIFFUSE MIDLINE GLIOMA","year":2020,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Radiomics; Gray level; Proportional hazards model; Concordance; Artificial intelligence; Radiology; Nuclear medicine; Internal medicine; Computer science; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0006896375,0.0005746742,0.0003520853,0.001291126,0.0001525243,0.0006208816,0.0003293194,0.0003523948,0.001835187],"category_scores_gemma":[0.001403561,0.0001168629,0.0002333727,0.0008141097,0.0002316975,0.0002375906,0.0003638177,0.0002757225,0.0005686382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178428,"about_ca_system_score_gemma":0.0002856682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761211,"about_ca_topic_score_gemma":0.001901125,"domain_scores_codex":[0.9997525,0.00005236356,0.00002070939,0.00007331405,0.00006083614,0.00004032398],"domain_scores_gemma":[0.9993396,0.0001118939,0.0002772207,0.00004449852,0.0001103144,0.0001164653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001059777,0.00008900095,0.9491938,0.0001215745,0.000120837,0.0002098617,0.00004266307,0.001929885,0.01046455,0.00009062488,0.001134978,0.03554255],"study_design_scores_gemma":[0.00003593837,0.0004419389,0.9749008,0.00003515222,0.0001533472,0.001175359,0.0001134024,0.01134136,0.008437963,0.0003557743,0.002989394,0.00001963666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913511,0.001760345,0.002161558,0.0001323796,0.00002217391,0.0000608058,0.003049103,0.0001210249,0.001341497],"genre_scores_gemma":[0.9944913,0.0002286425,0.002224278,0.0000355251,0.00002117301,0.00004240253,0.002478491,0.0000116798,0.0004665254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001835187,"threshold_uncertainty_score":0.006139338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612550626617609,"score_gpt":0.2940524861700402,"score_spread":0.2779269799038641,"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."}}