Hyperostosis in meningioma: a retrospective exploration of histological correlates
Bibliographic record
Abstract
Purpose Meningiomas are the most common type of primary brain tumour. Hyperostosis is commonly associated but remains incompletely understood. This study aimed to evaluate the relationship between meningioma-associated hyperostosis and other tumour variables.Materials and Methods We retrospectively analysed 245 patients with 263 cranial meningiomas (202 CNS WHO grade 1, 53 grade 2, and 8 grade 3) who underwent surgery over a three-year period. Meningiomas adjacent to the skull were included. Demographic, radiological, and tumour characteristics were analysed using standard statistical methods.Results Hyperostosis was evident in 99 (38%) of meningiomas. The most common subtypes were meningothelial, transitional, fibrous, atypical, and anaplastic. There were no statistically significant relationships between hyperostosis and bone invasion, and CNS WHO grade and histological subtype. Hyperostosis was more common in skull base meningiomas than in convexity meningiomas (p = 0.001). Ki-67 index was significantly related to CNS WHO grade but not histological subtype when grade was considered. Mean Ki-67 index was higher in meningiomas without hyperostosis (p = 0.03). There was no such relationship with bone invasion (p = 0.29). Univariate and multivariate analysis revealed that Ki-67 index was negatively correlated with hyperostosis (p = 0.03), while bone invasion (p < 0.001) and skull base location (p = 0.03) were positively correlated with hyperostosis.Conclusions Hyperostosis did not appear to be related to CNS WHO grade or histological subtype. Proliferative activity appeared to be higher in meningiomas without hyperostosis and hyperostosis was associated with evidence of bone invasion and skull base location.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".