Venous sinus stenting for cerebral venous congestion-induced trigeminal neuralgia: A case report
Bibliographic record
Abstract
BackgroundUncommon clinical manifestations of cerebral venous congestion syndrome (CVCS) are challenging for clinicians and may result in inappropriate treatment selection and incomplete clinical resolution. Although trigeminal neuralgia (TN) has been reported in association with CVCS, evidence of symptom resolution following venous sinus stenting (VSS) is lacking. We report a case in which VSS effectively alleviated TN.Case PresentationA middle-aged female patient presented with bilateral pulsatile tinnitus, papilledema, pressure headaches, as well as left-sided TN. Initial computerized tomography venography demonstrated bilateral transverse sinus stenosis and a prominent left mastoid emissary vein. Therefore, VSS was offered. Venous pressure measurements for extra- and intracranial veins were acquired, revealing a pressure gradient. Successful bilateral transverse sinus stenting was performed, resulting in a reduction of the pressure gradient in both sinuses. Endovascular stenting proved effective in managing CVCS symptomatology, including CVCS-induced TN. Residual left-sided pulsatile tinnitus due to the left mastoid emissary vein persisted.ConclusionThis case underscores the role of intracranial VSS in managing CVCS-associated symptoms, demonstrating its potential to relieve both typical and less common manifestations, including TN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".