Pathologic Characteristics Associated With Local Recurrence of Atypical Meningiomas Following Surgical Resection
Bibliographic record
Abstract
BACKGROUND: Optimal management of grade II meningiomas following resection remains controversial, owing mostly to the heterogeneity of post-operative (post-op) recurrence patterns across studies. Improved risk stratification of these patients would ensure that only those most at risk of recurrence would undergo appropriate post-op radiation therapy (RT). METHODS: Medical records from patients who underwent resection for grade II meningiomas were retrospectively reviewed. Demographic, disease characteristics, treatment, and clinical course data were retrospectively collected. Logistic regression, Cox proportional hazards modeling, and Kaplan-Meier curves with log rank testing were conducted to describe any potential relationships with time of recurrence. RESULTS: Of the 49 patients identified, 18 (36.7%) suffered a local recurrence following resection with a median follow-up of 3.1 years (range: 0.23 - 17.1 years). Past recurrence of the meningioma (P = 0.002) and extent of resection (P = 0.02) were significantly associated with local recurrence. On multivariable analysis, only prior meningioma recurrence was associated with time to local failure (P = 0.021). No histopathologic factors were found to be associated with the initial local failure. Of those who suffered a local recurrence, the presence of bone invasion (hazard ratio: 0.069, P = 0.008) and lack of salvage RT (P = 0.02) were associated with subsequent local failure. CONCLUSIONS: Currently considered histopathologic factors appear not to be helpful in guiding initial treatment course. History of prior local failure and bone invasion appear to be associated with multiple recurrences. Optimal surgical resection is critical to improving outcomes, and salvage RT may reduce subsequent local failure.
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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 | 0.004 |
| 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 | 0.000 | 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".