Evaluating vestibulo-ocular reflex gain and catch-up saccades following head impulses in normal aging
Bibliographic record
Abstract
BackgroundThe video head impulse test (vHIT) is vital in clinical setting for assessing vestibulo-ocular reflex (VOR) function in patients of all ages. However, how normal aging influence VOR gain and catch-up saccades remains unclear, thus leading to confusion in interpretation of vHIT results.ObjectiveThis study aims to compare VOR gain and saccades parameters (frequency, amplitude, and latency) between younger and older adults, while maintaining head velocity and acceleration within the same range.MethodsA total of 24 younger and 24 older adults performed horizontal vHIT tests (ICS Impulse, Otometrics, Denmark). Gain and saccades were analyzed using a custom MATLAB script. Three VOR gain algorithms were compared: Area under the curve (AUC), instantaneous gain, and regression gain.ResultsIn our sample, no significant differences in the VOR gains were observed between younger and older adults using any of the algorithms. Compared to younger adults, older adults had saccades that were significantly more frequent, of greater amplitude, and of shorter latencies. However, a larger sample size is needed to confirm the lack of aging effect on VOR gains.ConclusionsThe absence of significant effects of aging on VOR gain in vHIT demonstrates that all three gain algorithms should provide similar values for patients across all ages in clinical practice. The results suggest that small saccades in older adults are unrelated to head impulse parameters, and the mechanisms behind this increase in saccades with normal aging remain to be explored.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".