Continuous Head Motion is a Greater Motor Control Challenge than Transient Head Motion in Patients with Loss of Vestibular Function
Bibliographic record
Abstract
Background. The vestibular system is vital for gaze stability via the vestibulo-ocular reflex, which generates compensatory eye motion in the direction opposite to head motion. Consequently, individuals with peripheral vestibular loss demonstrate impaired gaze stability that reduces functional capacity and quality of life. To facilitate patients’ compensatory strategies, two classes of gaze stabilization exercises are often prescribed: (i) transient (eg, ballistic) and (ii) continuous. However, the relative benefits of these two classes of exercises are not well understood. Objective. To quantify head motion kinematics in patients with vestibular loss while they performed both classes of exercises. Methods. Using inertial measurement units, head movements of 18 vestibular schwannoma patients were measured before and after surgical deafferentation and compared with age-matched controls. Results. We found that the head movement during both classes of exercises paralleled those of natural head movement recorded during daily activities. However, head movement patterns were more informative for continuous than transient exercises in distinguishing patients from healthy controls. Specifically, we observed coupling between kinematic measures in control subjects that was absent in patients for continuous but not transient head motion exercises. In addition, kinematic measures (eg, cycle duration) were predictive of standard clinical measures for continuous but not transient head motion exercises. Conclusions. Our data suggest that performing continuous head motion is a greater motor control challenge than transient head motion in patients with less reliable vestibular feedback during the sub-acute stage of recovery, which may also prove to be a reliable measure of progression in vestibular rehabilitation protocols.
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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".