{"id":"W4408171770","doi":"10.59275/j.melba.2025-bea1","title":"Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge","year":2025,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Toronto; McGill University; University of Ottawa; Montreal Neurological Institute and Hospital","funders":"","keywords":"Meningioma; Medicine; Radiology","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":[0.004740178,0.002539458,0.001300884,0.001943607,0.001255547,0.001969459,0.002469177,0.002357339,0.002555211],"category_scores_gemma":[0.01218117,0.0005339374,0.001655625,0.001410256,0.0009009989,0.0011444,0.002416845,0.001695794,0.003358391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002880797,"about_ca_system_score_gemma":0.002631877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234292,"about_ca_topic_score_gemma":0.04104444,"domain_scores_codex":[0.9950669,0.001026927,0.0002416911,0.00109541,0.002138589,0.0004304249],"domain_scores_gemma":[0.9943663,0.001613267,0.0003145302,0.0007546462,0.002292462,0.0006586527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.004627257,0.002831119,0.04075944,0.003364348,0.001625283,0.002661613,0.001315926,0.143673,0.05076104,0.00502505,0.4078926,0.3354634],"study_design_scores_gemma":[0.001128845,0.002590664,0.06568246,0.0003582168,0.0005275146,0.003389657,0.001853075,0.6961723,0.06959108,0.009844078,0.1485676,0.0002945444],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8127232,0.006299654,0.06868453,0.005166654,0.001894778,0.002645689,0.05626978,0.01781242,0.02850344],"genre_scores_gemma":[0.5872027,0.001014511,0.1424653,0.002335324,0.0005258821,0.001602718,0.2443727,0.002965282,0.01751556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0234292,"threshold_uncertainty_score":0.04658568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167730608787076,"score_gpt":0.3034014316426765,"score_spread":0.2917241255548058,"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."}}