Correlation between Ocular and Vestibular Abnormalities and Convergence Insufficiency in Post-Concussion Syndrome
Bibliographic record
Abstract
The vestibular and oculomotor/visual systems are commonly affected in post-concussion syndrome (PCS). Convergence insufficiency (CI) is the most common ocular abnormality after concussion. Electrovestibulography (EVestG) is a relatively new non-invasive method that measures the peripheral vestibular responses; it has shown abnormal vestibular responses in a PCS. Here, we report the results of investigating the correlation between the vestibular and oculomotor systems in PCS population using EVestG and CI measures. Forty-eight PCS patients were tested using EVestG, out of which 20 also completed the Rivermead post-concussion questionnaire (RPQ). An EVestG feature (Field Potential (FP)-area) was extracted from the stationary part of the EVestG signals. A neuro-ophthalmologist (author BM) measured participants’ CI at near vision using cross-cover examination and a prism-bar. Results indicate: (1) vestibular abnormality (i.e. FP-area) and CI values are significantly correlated in PCS (R = 0.68, p < .01), and (2) there are significant correlations between severity of concussion (i.e. RPQ3) and CI (R = 0.70, p < .01) and between RPQ3 and FP-area (R = −0.56, p < .02). To the best of our knowledge, this is the first study that objectively demonstrates a significant positive correlation between the CI and vestibular systems’ abnormality. These findings are scientifically important as they help localise the pathology of PCS, and are clinically valuable as they help physicians in their decision-making about PCS diagnosis and rehabilitation strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".