Insights on Survival and Recurrence After Surgery in Malignant Meningiomas: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Malignant meningiomas (WHO Grade III) are rare, aggressive tumors with poor prognosis and high recurrence rates. Gross total resection (GTR) is the preferred treatment; however, recurrence remains a challenge, especially after subtotal resection (STR). The role of adjuvant radiotherapy (RT) and chemotherapy in improving patient outcomes remains controversial. This systematic review and meta-analysis assessed the impact of surgical extent, adjuvant therapies, and prognostic factors on survival and recurrence of malignant meningiomas. METHODS: A systematic review and meta-analysis were conducted using the PubMed, Cochrane Library, and Scopus databases. Eligible studies included retrospective and prospective cohorts, case-control studies, and clinical trials reporting the surgical extent (GTR vs. STR), adjuvant therapy, survival, and recurrence. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Cochrane Risk of Bias Tool. Meta-analysis was performed using random- and fixed-effects models and heterogeneity was assessed using the I² statistic. RESULTS: Sixteen studies (2,208 patients) met the inclusion criteria. The 5-year overall survival (OS) ranged from 40% to 90%, with GTR significantly improving survival (HR = 0.54, 95% CI: 0.50-0.58, p < 0.00001) [1]. Recurrence rates were lower in GTR cases (50-90% in STR). Adjuvant RT improved progression-free survival (HR = 0.36, 95% CI: 0.18-0.70) in STR patients, but its benefit post-GTR was unclear. Chemotherapy had no significant effect on patient survival [2]. Key prognostic factors included tumor location, patient age, Ki-67 index, and histology [3, 4]. CONCLUSION: GTR is the strongest predictor of long-term survival, whereas STR requires adjuvant RT for disease control. The role of chemotherapy remains uncertain, necessitating further research into targeted therapies. Standardized treatment protocols and long-term surveillance are essential to improve patient outcomes [5].
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".