{"id":"W4416141169","doi":"10.1093/neuonc/noaf201.0051","title":"EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS","year":2025,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"DNA methylation; Epigenetics; Meningioma; Random forest; Epigenomics; Clinical Practice; Brain tumor; Methylation","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":[0.001709911,0.0006468777,0.0006479569,0.002037184,0.0005218071,0.00158198,0.0006014414,0.0006361455,0.005346922],"category_scores_gemma":[0.002582157,0.0002326538,0.000973432,0.001424895,0.0003749223,0.0005646736,0.0008474943,0.0006510243,0.001657082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008027228,"about_ca_system_score_gemma":0.001326616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003777338,"about_ca_topic_score_gemma":0.004138999,"domain_scores_codex":[0.9990644,0.0001582946,0.00007718006,0.0003355606,0.0002282897,0.0001362874],"domain_scores_gemma":[0.9987741,0.0004036322,0.000214799,0.0002395783,0.0002475507,0.0001203119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00222083,0.0007391458,0.3722375,0.001156603,0.0009002736,0.001092628,0.000222427,0.04027416,0.3310789,0.01144687,0.02338707,0.2152435],"study_design_scores_gemma":[0.0002232925,0.0006718514,0.4509856,0.0001375389,0.0004959879,0.001291496,0.0002187002,0.390024,0.1067543,0.01203298,0.03702575,0.0001385497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7290043,0.001486741,0.1960402,0.001348327,0.0001902154,0.0009063362,0.05047056,0.005038634,0.01551485],"genre_scores_gemma":[0.8328518,0.0004293497,0.1207032,0.0004198656,0.00006446485,0.0009547558,0.03934823,0.0005125643,0.004715963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005346922,"threshold_uncertainty_score":0.01788723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095926959770587,"score_gpt":0.3384206186877567,"score_spread":0.3074613490900508,"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."}}