Head movement kinematics are altered during gaze stability exercises in vestibular schwannoma patients
Bibliographic record
Abstract
Gaze stability is the ability of the eyes to fixate a stable point when the head is moving in space. Because gaze stability is impaired in peripheral vestibular loss patients, gaze stabilization exercises are often prescribed to facilitate compensation. However, both the assessment and prescription of these exercises are subjective. Accordingly, here we quantified head motion kinematics in patients with vestibular loss while they performed the standard of care gaze stability exercises, both before and after surgical deafferentation. We also correlate the head kinematic data with standard clinical outcome measures. Using inertial measurement units, we quantified head movements in patients as they transitioned through these two vestibular states characterized by different levels of peripheral damage. Comparison with age-matched healthy control subjects revealed that the same kinematic measurements were significantly abnormal in patients both pre- and post-surgery. Regardless of direction, patients took a longer time to move their heads during the exercises. Interestingly, these changes in kinematics suggest a strategy that existed preoperatively and remained symmetric after surgery although the patients then had complete unilateral vestibular loss. Further, we found that this kinematic assessment was a good predictor of clinical outcomes, and that pre-surgery clinical measures could predict post-surgery head kinematics. Thus, together, our results provide the first experimental evidence that patients show significant changes in head kinematics during gaze stability exercises, even prior to surgery. This suggests that early changes in head kinematic strategy due to significant but incomplete vestibular loss are already maladaptive as compared to controls.
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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) | 0.001 | 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".