{"id":"W4404238114","doi":"10.1093/neuonc/noae165.1159","title":"TMET-21. METABOLIC PROFILING OF MENINGIOMA REVEALS NOVEL SUBGROUP-SPECIFIC BIOLOGIC INSIGHTS AND OUTCOME DEPENDENCIES","year":2024,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Medical Imaging and Pathology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Profiling (computer programming); Computational biology; Medicine; Biology; Computer science; Programming language","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":[0.0002735636,0.0003047405,0.0003443052,0.0003934529,0.0001703149,0.0004167936,0.0001440202,0.0001865873,0.0009602362],"category_scores_gemma":[0.0003562307,0.00008681465,0.0003135065,0.0004336716,0.0001550039,0.0001592975,0.0002118694,0.0003449354,0.0002420001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003851828,"about_ca_system_score_gemma":0.0002626307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103782,"about_ca_topic_score_gemma":0.001959623,"domain_scores_codex":[0.9998691,0.00001987653,0.000008886621,0.00004349033,0.00002885593,0.00002972256],"domain_scores_gemma":[0.9998106,0.00002851901,0.00007762738,0.0000216544,0.00001938635,0.00004214254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.008514214,0.0002088809,0.4630331,0.0002774193,0.0005160019,0.0006366546,0.0001079638,0.002538544,0.4671712,0.0002320544,0.001050104,0.05571407],"study_design_scores_gemma":[0.00008565543,0.00151886,0.8775163,0.00001829917,0.0004257936,0.00175665,0.0001491106,0.005784654,0.10789,0.0009553973,0.00387406,0.00002532854],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956127,0.000973111,0.0009830041,0.00008426944,0.00000964466,0.00001474075,0.001724128,0.00004641186,0.0005519801],"genre_scores_gemma":[0.9970182,0.0002327524,0.0005245627,0.00003396161,0.000007415468,0.00001488859,0.001664478,0.00001035658,0.0004935228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00103782,"threshold_uncertainty_score":0.003212273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08379690202335066,"score_gpt":0.353893869957009,"score_spread":0.2700969679336583,"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."}}