{"id":"W2948500453","doi":"10.1093/neuonc/noz061","title":"DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management","year":2019,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":274,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; Public Health Ontario; University of Toronto","funders":"National Cancer Institute; National Center for Advancing Translational Sciences; Brain Tumour Charity","keywords":"Nomogram; Oncology; DNA methylation; Hazard ratio; Medicine; Internal medicine; Proportional hazards model; Meningioma; Bioinformatics; Surgery; Confidence interval; Biology; 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":[0.006253601,0.001058918,0.0008669044,0.002420109,0.0003819228,0.001258799,0.0007321973,0.0006068037,0.0005089546],"category_scores_gemma":[0.0115005,0.0002255195,0.001104013,0.0007852161,0.0003283557,0.0006293086,0.0007856839,0.001048829,0.0003002543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008289863,"about_ca_system_score_gemma":0.000888816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765578,"about_ca_topic_score_gemma":0.004648308,"domain_scores_codex":[0.9988334,0.0005586628,0.00009591313,0.0002077873,0.0002381017,0.00006604409],"domain_scores_gemma":[0.9953629,0.002292957,0.001078346,0.0003343954,0.0006347065,0.0002966994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006791121,0.0001098707,0.8608866,0.0001055933,0.0005670667,0.0001353159,0.0001439702,0.03222144,0.002816864,0.0004073367,0.002206103,0.0997207],"study_design_scores_gemma":[0.0001114912,0.001077308,0.4162712,0.0002025557,0.0008520733,0.0009149706,0.0003340497,0.5646885,0.007637633,0.003544605,0.004251725,0.0001137396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8697353,0.00369027,0.119914,0.001750452,0.00009650098,0.0002152781,0.001791265,0.0008302241,0.001976665],"genre_scores_gemma":[0.9731424,0.0004728163,0.02538663,0.00008220947,0.00003461995,0.00006397066,0.0006040626,0.00003550166,0.0001777841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006253601,"threshold_uncertainty_score":0.03307259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04341833953045481,"score_gpt":0.348758398496677,"score_spread":0.3053400589662222,"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."}}