Prevalence of Venous Sinus Stenosis in Spontaneous Cerebrospinal Fluid Leak and the Role for Venous Sinus Stenting: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background Given the association between spontaneous cerebrospinal fluid (sCSF) leak and idiopathic intracranial hypertension (IIH), and the association of IIH and venous sinus stenosis (VSS), we sought to determine the prevalence of VSS in sCSF leak and the role of venous sinus stenting for sCSF leak. Methods A protocol was registered in PROSPERO [CRD42024568270]. A search was conducted in four electronic databases: Medline (Ovid), Embase, CINAHL, Web of Science, and Cochrane CENTRAL. Two reviewers independently screened citations and extracted data. Methodological quality was assessed using the Joanna Briggs Institute's critical appraisal tool. Data were pooled using a random effects model to calculate overall prevalence and relative risk (RR). Results Fifteen studies met the final inclusion criteria, with a total of 372 patients presenting with sCSF leak. The pooled prevalence of VSS in patients with sCSF leak was 0.71 (95% CI: 0.56–0.83, I2 = 74%). VSS was three‐fold greater in patients with sCSF leak compared to those without, although this was not statistically significant (RR = 3.11; 95% CI: 0.64–15.27, I2 = 87%). Ninety percent of patients with VSS who underwent venous sinus stenting as adjunctive therapy to surgical repair of sCSF leak or for medically refractive IIH demonstrated resolution of symptoms without sCSF leak recurrence at last follow‐up. Conclusion VSS is common in patients with sCSF leak, and adjunctive venous sinus stenting after surgical leak repair may benefit a subset of patients. Further studies are needed to clarify the role of venous sinus stenting in conjunction with surgical repair of leaks.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".