The impact of brain invasion criteria on the incidence and distribution of WHO grade 1, 2, and 3 meningiomas
Bibliographic record
Abstract
BACKGROUND: In 2016 brain invasion was added as a standalone diagnostic criterion for Grade 2 meningiomas in the WHO Classification of Brain Tumors. The aim of this study was to compare the incidence and distribution of meningiomas, and agreement, between the 2007 and 2016 WHO criteria. METHODS: All cases of intracranial meningiomas diagnosed between 2007 and 2020 at a tertiary care academic hospital were identified. The incidence of each meningioma grade in the WHO 2007 and WHO 2016 cohorts were compared. Additionally, each case in the 2007 cohort was re-graded according to the WHO 2016 criteria to determine the intra-class correlation (ICC) between criteria. RESULTS: Of 814 cases, 532 (65.4%) were in the 2007 WHO cohort and 282 (34.6%) were in the 2016 WHO cohort. There were no differences in the distribution of meningioma grades between cohorts (P = .11). Incidence rates were: 75.0% vs. 75.2% for Grade 1, 22.7% vs. 24.5% for Grade 2, and 2.3% vs. 0.4% for Grade 3, for the 2007 and 2016 cohorts, respectively. Upon re-grading, 21 cases (3.9%) were changed. ICC between original and revised grade was 0.92 (95% CI: 0.91-0.93). Amongst Grade 2 meningiomas with brain invasion, 75.8% had three or more atypical histologic features or an elevated mitotic index. CONCLUSIONS: Including brain invasion as a standalone diagnostic criterion for Grade 2 meningiomas had minimal impact on the incidence of specific meningioma grade tumors. There is strong agreement between the 2007 and 2016 WHO criteria, likely due to cosegregation of grade elevating features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".