GP.6 The Impact of Brain Invasion on Intracranial Meningioma Grading
Bibliographic record
Abstract
Background: In 2016, the WHO Classification of Brain Tumors included brain invasion as a standalone diagnostic criterion for grade 2 meningioma diagnosis. In this study we explored the impact of this change on the incidence and distribution of meningioma grades. Methods: All cases of meningiomas diagnosed from 2007-2020 at a tertiary care hospital were identified. The distribution of meningioma grades before (WHO 2007) and after (WHO 2016) the introduction of the 2016 WHO criteria were compared. Each case in the 2007 cohort was re-graded according to the 2016 criteria to determine the intra-class correlation (ICC) between grading 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=0.11). Upon re-grading, 21 cases (3.9%) were changed. ICC between original and revised grade was 0.92 (95% CIs: 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: Brain invasion alone has minimal impact on the incidence or distribution of specific meningioma grade tumors, likely due to cosegregation of grade elevating features.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".