Concussion can increase the risk of visually induced motion sickness
Bibliographic record
Abstract
Concussion can lead to various symptoms such as balance problems, memory impairments, dizziness, and/or headaches. It has been previously suggested that during self-motion relevant tasks, individuals with concussion may rely heavily on visual information to compensate for potentially less reliable vestibular inputs and/or problems with multisensory integration. As such, concussed individuals may also be more sensitive to other visually-driven sensations such as visually induced motion sickness (VIMS). To investigate whether concussed individuals are at elevated risk of experiencing VIMS, we exposed participants with concussion (n = 16) and healthy controls (n = 15) to a virtual scene depicting visual self-motion down a grocery store aisle at different speeds. Participants with concussion were further separated into symptomatic and asymptomatic groups. VIMS was measured with the SSQ before and after stimulus exposure, and visual dependence, self-reported dizziness, and somatization were recorded at baseline. Results showed that concussed participants who were symptomatic demonstrated significantly higher SSQ scores after stimulus presentation compared to healthy controls and those who were asymptomatic. Visual dependence was positively correlated with the level of VIMS in healthy controls and participants with concussion. Our results suggest that the presence of concussion symptoms at time of testing significantly increased the risk and severity of VIMS. This finding is of relevance with regards to the use of visual display devices such as Virtual Reality applications in the assessment and rehabilitation of individuals with concussion.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".