Effects of dimenhydrinate on computerized dynamic posturography.
Bibliographic record
Abstract
OBJECTIVE: Previous studies have demonstrated that sedatives and antiemetics commonly used by patients suppress the vestibulo-ocular reflexes during electronystagmography, making it more difficult to quantify function in such patients. The effects of these medications on computerized dynamic posturography (CDP) have not been studied, and the influences, if any, on the vestibulospinal reflexes are not known. We aimed to study the influence, if any, of dimenhydrinate on CDP performance. DESIGN: A double-blinded study using a randomized protocol to compare the effects of dimenhydrinate and placebo on CDP performance in normal subjects. SETTING: A tertiary/quaternary care facility using a standardized CDP assessment protocol. METHODS: After a CDP training session (one assessment) to rule out any learning effect, 10 subjects underwent CDP assessment on 2 separate days after ingestion of either a standard single dose of dimenhydrinate or placebo. MAIN OUTCOME MEASURES: Pre- and post-medication CDP performance was measured using Sensory Organization Test (SOT) composite scores and also scores on CDP conditions particularly sensitive to measurement of vestibular impairment (SOT conditions 5 and 6). RESULTS: Analysis of data showed no significant effect on CDP performance of normal subjects after dimenhydrinate administration, although there may be a trend toward a slight effect on performance. CONCLUSIONS: There seems to be no significant effect of dimenhydrinate on CDP performance in normal subjects, although there may be a trend. Further studies using a dose causing more significant symptoms or using a higher number of subjects may clarify this.
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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 | 0.000 | 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) | 0.003 | 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".