Vestibular patients generate more regular head movements than healthy individuals during gaze-stabilization exercises
Bibliographic record
Abstract
The vestibular system is vital for maintaining stable vision during daily activities. When peripheral vestibular input is lost, patients initially experience impaired gaze stability due to reduced effectiveness of the vestibular-ocular-reflex pathway. To aid rehabilitation, patients are often prescribed gaze-stabilization exercises during which they make self-initiated active head movements. Analyzing statistical pattern of sequences of stereotyped behaviors to characterize degrees of randomness or repeatability has proven to be a powerful approach for diagnosing disease states, yet this approach has not been applied to patients with vestibular loss. Accordingly, here we investigated whether the patterning of head movements is altered in vestibular-loss patients by using trial-based analysis and sample-entropy measurement. The subjects completed gaze-stability exercises in both the yaw and pitch directions. In trial-based analysis, we calculated the trial-to-trial variability of head movement duration and peak velocity for each individual head movement. Our results showed that healthy individuals exhibited a temporally repetitive (correlated) structure in peak velocity, especially for head movements in the pitch direction, which was absent in most patients. In the sample entropy analysis, which measures the irregularity or randomness of a time series, our results revealed that head-movement generation was more regular in vestibular-loss patients compared to healthy controls. Together, these analyses suggest that vestibular-loss patients display less flexibility in the patterning of their head motions. Our results provide the first experimental evidence that temporal head stability is a valuable metric for distinguishing individuals with impaired vestibular function from healthy ones.
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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) | 0.002 | 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".