Post-traumatic Peripheral Vestibular Disorders (Excluding Positional Vertigo): Institutional Experience from University Health Network’s Workplace Safety and Insurance Board Head-Injury Program
Bibliographic record
Abstract
Abstract Purpose: Benign paroxysmal positional vertigo has typically been reported to be the most common cause of post-traumatic dizziness. There is however paucity in the literature about other peripheral vestibular disorders post-head injury. This article provides an overview of other causes of non-positional dizziness post-head trauma from our large institutional experience.Methods: The UHN WSIB Neurotology database (n=4,291) between 1998 and 2018 was retrospectively studied for those head-injured workers presenting with non-positional peripheral vestibular disorders. All subjects had a detailed neurotological history and examination and vestibular testing including video nystagmography, video head impulse testing (or a magnetic scleral search coil study), vestibular-evoked myogenic potentials, and audiometry. Imaging studies included routine brain and high-resolution temporal bone CT scans and/or brain MRI.Results: Based on a database of 4,291 head-injured workers with dizziness, 244 were diagnosed with non-positional peripheral vertigo. Recurrent vestibulopathy (RV) was the most common cause of non-positional post-traumatic vertigo. The incidence of Meniere’s disease in the post-traumatic setting did not appear greater than found in the general population. Conclusion: The clinical spectrum pertaining to recurrent vestibulopathy, Meniere’s disease, delayed endolymphatic hydrops, drop attacks, superior semicircular canal dehiscence syndrome, and uncompensated peripheral vestibular loss are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".