Mitotic Index is an Independent Predictor of Recurrence‐Free Survival in Meningioma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it