Torrents of torment: turbulence as a mechanism of pulsatile tinnitus secondary to venous stenosis revealed by high-fidelity computational fluid dynamics
Bibliographic record
Abstract
BACKGROUND: Pulsatile tinnitus (PT) is a debilitating condition that can be caused by a vascular abnormality, such as an arterial or venous lesion. Although treatment of PT-related venous lesions has been shown to successfully cure patients of the associated 'tormenting' rhythmical sound, much controversy still exists regarding their role in the etiology of PT. METHODS: A patient presented with a history of worsening, unilateral PT. A partial venous sinus obstruction related to the large arachnoid granulation was detected on the right side, and subsequently stented at the right transverse sinus. High-fidelity computational fluid dynamics (CFD) was performed on a 3D model digitally segmented from the pre-stent venogram, with assumed pulsatile flow rates. A post-stent CFD model was also constructed from this. Data-driven sonification was performed on the CFD velocity data, blinded to the patient's self-reported sounds. RESULTS: The patient reported that the PT was completely resolved after stenting, and has had no recurrence of the symptoms after more than 2 years. CFD simulation revealed highly disturbed, turbulent-like flow at the sigmoid sinus close to auditory structures, producing a sonified audio signal that reproduced the subjective sonance of the patient's PT. No turbulence or sounds were evident at the stenosis, or anywhere in the post-stent model. CONCLUSIONS: For the first time, turbulence generated distal to a venous stenosis is shown to be a cause of PT. High-fidelity CFD may be useful for identifying patients with such 'torrents' of flow, to help guide treatment decision-making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".