{"id":"W3111444730","doi":"10.1093/neuonc/noaa222.348","title":"IMG-13. MRI-BASED RADIOMICS PROGNOSTIC MARKERS OF POSTERIOR FOSSA EPENDYMOMA","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; Hospital for Sick Children","funders":"","keywords":"Ependymoma; Medicine; Concordance; Radiomics; Magnetic resonance imaging; Radiology; Artificial intelligence; Internal medicine; Computer science","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":[0.002096515,0.001001186,0.0006683363,0.0011858,0.0001957206,0.0009011836,0.0004062837,0.0005136044,0.001928626],"category_scores_gemma":[0.002424513,0.0001838564,0.0006682382,0.0006156968,0.0002837756,0.0002999367,0.0004562692,0.0004976867,0.001135506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004568626,"about_ca_system_score_gemma":0.000574266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002436809,"about_ca_topic_score_gemma":0.0024128,"domain_scores_codex":[0.9994906,0.0001776916,0.00003536005,0.0001306683,0.0001051486,0.00006042856],"domain_scores_gemma":[0.9991775,0.0002918311,0.0002177132,0.00006055094,0.0001578037,0.00009451147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001457471,0.0003229404,0.8298965,0.0001475826,0.0004594674,0.0003497741,0.00006135463,0.03456822,0.01336077,0.0002321175,0.003515044,0.1156289],"study_design_scores_gemma":[0.0001054711,0.001037231,0.507972,0.000100733,0.0003508685,0.001209623,0.0001197869,0.4705786,0.01385027,0.0009806792,0.003635144,0.00005961031],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706767,0.001419518,0.02043412,0.0003757393,0.00006391609,0.000112455,0.004438355,0.000700512,0.001778846],"genre_scores_gemma":[0.9832453,0.0001806234,0.0113045,0.00006644123,0.00003027752,0.0000729502,0.004107394,0.0000374752,0.0009551976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002436809,"threshold_uncertainty_score":0.0110876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653222282427767,"score_gpt":0.2939588224994928,"score_spread":0.2774265996752152,"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."}}