{"id":"W2982733458","doi":"10.1093/neuonc/noz175.1132","title":"TMOD-33. AN INTEGRATED APPROACH COMBINING MATHEMATICAL AND GENOMIC METHODS TO REVEAL THE OPTIMAL TIMING OF THERAPEUTIC INTERVENTION IN WHO GRADE II DIFFUSE GLIOMA","year":2019,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glioma; Radiation therapy; Oncology; Somatic cell; Fluid-attenuated inversion recovery; Pathological; Temozolomide; Medicine; Germline mutation; Internal medicine; Proportional hazards model; Mutation; Pathology; Biology; Cancer research; Magnetic resonance imaging; Radiology; Genetics; Gene","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":[0.0008157172,0.0005796573,0.0006331586,0.0007051381,0.0001948696,0.0006965346,0.0004446787,0.0004129778,0.001753845],"category_scores_gemma":[0.001532692,0.0003312107,0.0008658391,0.0003888805,0.0002683915,0.0003680028,0.0005197563,0.0003622439,0.0002205174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036738,"about_ca_system_score_gemma":0.001123577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003930505,"about_ca_topic_score_gemma":0.003730402,"domain_scores_codex":[0.999873,0.00005149791,0.000007397613,0.00003046423,0.00002311222,0.00001442297],"domain_scores_gemma":[0.9996167,0.0002088233,0.00008479875,0.00002371347,0.00004328098,0.00002259068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002442533,0.00008548493,0.01154596,0.0001974925,0.0001582455,0.0001420797,0.00003637669,0.903193,0.009517223,0.01199959,0.0008268324,0.06205347],"study_design_scores_gemma":[0.000007602061,0.00003750064,0.0006727661,0.000004513721,0.0000130542,0.00002206917,0.000007147109,0.996511,0.0005845391,0.00174878,0.0003849101,0.000006089338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1400138,0.0008388922,0.8536288,0.0004391102,0.00005990194,0.0001414544,0.0007165708,0.001020713,0.003140588],"genre_scores_gemma":[0.740214,0.0004539769,0.2561586,0.00009613545,0.00003048416,0.0002635236,0.0005691822,0.0001917418,0.002022274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003930505,"threshold_uncertainty_score":0.007815242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05572562521701402,"score_gpt":0.36968547767997,"score_spread":0.313959852462956,"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."}}