Central Vestibular Dysfunction in Head Injury
Bibliographic record
Abstract
OBJECTIVES: This study aims to provide an overview of dizziness post head injury in those with prominent features for central vestibular dysfunction (CVD) in comparison to those with a post-traumatic peripheral vestibular etiology. STUDY DESIGN: Retrospective. SETTING: University Health Network (UHN) Workplace Safety and Insurance Board (WSIB) database from 1988 to 2018 were evaluated for post-traumatic dizziness. METHODS: The UHN WSIB neurotology database (n = 4291) between 1998 and 2018 was retrospectively studied for head-injured workers presenting with features for CVD associated with trauma. All patients had a detailed neurotological history and examination, audiovestibular testing that included video nystagmography (VNG) and cervical vestibular-evoked myogenic potentials (cVEMPs). Imaging studies including routine brain and high-resolution temporal bone computed tomography (CT) scans and/or intracranial magnetic resonance imaging (MRI) were available for the majority of injured workers. RESULTS: Among 4291 head-injured workers with dizziness, 23 were diagnosed with features/findings denoting CVD. Complaints of imbalance were significantly more common in those with CVD compared to vertigo and headache in those with peripheral vestibular dysfunction. Atypical positional nystagmus, oculomotor abnormalities and facial paralysis were more common in those with CVD. CONCLUSION: Symptomatic post-traumatic central vestibular injury is uncommon. It occurred primarily following high-impact trauma and was reflective for a more severe head injury where shearing effects on the brain often resulted in diffuse axonal injury. Complaints of persistent imbalance and ataxia were more common than complaints of vertigo. Eye movement abnormalities were highly indicative for central nervous system injury even in those with minimal change on CT/MRI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".