{"id":"W4200240386","doi":"10.1093/neuonc/noab196.525","title":"NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING","year":2021,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Glioma; Fluid-attenuated inversion recovery; Nuclear medicine; Artificial intelligence; Radiology; Computer science; Magnetic resonance imaging; Cancer research","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":[],"consensus_categories":[],"category_scores_codex":[0.000243884,0.0001485049,0.0003397386,0.000100918,0.0001788131,0.00002576725,0.00009438023,0.00008104667,0.00006119392],"category_scores_gemma":[0.0009356184,0.0001007528,0.00004952346,0.0002971932,0.0003180582,0.00004036822,0.0001114807,0.0007316269,0.000004639168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000620281,"about_ca_system_score_gemma":0.0002992102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004177256,"about_ca_topic_score_gemma":0.0000161815,"domain_scores_codex":[0.9985442,0.000285283,0.0003005924,0.0003739682,0.0002311702,0.0002647825],"domain_scores_gemma":[0.9984066,0.0007512394,0.0002582879,0.0002370686,0.0002134351,0.0001333208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001023102,0.00007696174,0.0855919,0.00005702311,0.00002808893,0.0008112301,0.0005759131,0.00001360124,0.7398256,0.0001215874,0.00004637154,0.1727494],"study_design_scores_gemma":[0.01013555,0.002474309,0.4177682,0.0003198212,0.0009954319,0.02000939,0.005192716,0.4253634,0.05045391,0.0002539963,0.06645593,0.0005773711],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843971,0.001188156,0.004634507,0.00744363,0.0001891789,0.0002236951,5.112154e-7,0.00004611468,0.001877149],"genre_scores_gemma":[0.9941371,0.0003045538,0.004226786,0.001042877,0.0001689179,0.00001292636,0.00001221788,0.00003714177,0.00005748988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6893716,"threshold_uncertainty_score":0.4108578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008333367874437536,"score_gpt":0.2677057116391958,"score_spread":0.2593723437647583,"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."}}