Traumatic Brain Injury and Risk of Intracranial Meningioma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Meningiomas are the most common primary intracranial tumors, but the role of traumatic brain injury (TBI) as a risk factor remains unclear. This study aimed to systematically evaluate and quantify the association between TBI and meningioma risk. Methods: We conducted a systematic review and meta-analysis of observational studies and Mendelian randomization analyses following PRISMA guidelines. PubMed, Scopus, Web of Science, Embase, and Cochrane databases were searched through December 5, 2025. Studies comparing meningioma incidence in individuals with and without a history of TBI were included. Risk of bias was assessed using the Newcastle-Ottawa Scale and ROBINS-MR tool. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using random-effects models. Heterogeneity and sensitivity analyses were performed. Results: Eleven studies (seven case-control, three cohort, one Mendelian randomization) were included, with eight contributing to the quantitative synthesis. The pooled analysis demonstrated a statistically significant association between TBI and meningioma (OR = 1.96; 95% CI [1.31 to 2.95]; p = 0.0012) with moderate heterogeneity (I² = 65.6%). Sensitivity analyses confirmed the robustness of the findings, although funnel plot asymmetry suggested potential publication bias. Conclusion: While a statistical association exists between TBI and meningioma, current evidence suggests it is unlikely to be causal. The association is primarily driven by retrospective case-control studies prone to bias, whereas prospective and genetic studies do not support a direct etiological link. Further long-term prospective studies are warranted to fully elucidate this relationship.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".