Motion Sickness and Vestibular Hypersensitivity
Bibliographic record
Abstract
OBJECTIVE: Motion sickness is poorly understood, although it has been recognized for years as debilitating. Vestibular function is required for motion sickness to occur, but motion sickness can also be brought on without body motion. The aim of this study was to see if there was a correlation between caloric response and motion sickness susceptibility. DESIGN: One experiment was a prospective study carried out on 200 patients. A second prospective study was carried out on 121 patients. SETTING: Patients referred to our tertiary/quaternary care dizziness clinic. METHODS: In experiment 1, caloric scores in patients were correlated with symptoms of motion sickness as established by responses to a simple question. In experiment 2, caloric scores were correlated with symptomatic responses to caloric testing itself. MAIN OUTCOME MEASURES: Caloric responses of the best ear were measured according to standardized caloric evaluation methods. RESULTS: There was no correlation between motion sickness and caloric scores. There was a significant difference in caloric scores between patients made symptomatic by calorics and those who were not. CONCLUSIONS: The autonomic response seen in some patients is not triggered by a specific level of semicircular canal response (as measured by caloric testing). We hypothesize that (similar to space motion sickness) the trigger is a signal differential that arises between semicircular canals and otoliths and that some patients are unable to suppress this response. These patients often suffer motion sickness on a long-term basis.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".