Mitotic Index is an Independent Predictor of Recurrence‐Free Survival in Meningioma
Bibliographic record
Abstract
While World Health Organization (WHO) grading of meningioma stratifies patients according to recurrence risk overall, there is substantial within-grade heterogeneity with respect to recurrence-free survival (RFS). Most meningiomas are graded according to mitotic counts per unit area on hematoxylin and eosin sections, a method potentially confounded by tumor cellularity, as well as potential limitations of accurate mitotic figure detection on routine histology. To refine mitotic figure assessment, we evaluated 363 meningiomas with phospho-histone H3 (Ser10) and determined the mitotic index (number of mitoses per 1000 tumor cells). The median mitotic indices among WHO grade I (n = 268), grade II (n = 84) and grade III (n = 11) tumors were 1, 4 and 12. Classification and regression tree analysis to categorize cut-offs identified three subgroups defined by mitotic indices of 0-2, 3-4 and ≥5, which on univariate analysis were associated with RFS (P < 0.01). In multivariate analysis, mitotic index subgrouped in this manner was significantly associated with RFS (P < 0.01) after adjustment for Simpson grade, WHO grade and MIB-1 index. Mitotic index was then examined within individual WHO grade, showing that for grade I and grade II meningiomas, mitotic index can add additional information to RFS risk. The results suggest that the use of a robust mitotic marker in meningioma could refine risk stratification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".