Effect of vestibular loss on head-on-trunk stability in individuals with vestibular schwannoma
Bibliographic record
Abstract
The vestibulo-collic reflex generates neck motor commands to produce head-on-trunk movements that are essential for stabilizing the head relative to space. Here we examined the effects of vestibular loss on head-on-trunk kinematics during voluntary behavior. Head and trunk movements were measured in individuals with vestibular schwannoma before and then 6 weeks after unilateral vestibular deafferentation via surgical resection of the tumor. Movements were recorded in 6 dimensions (i.e., 3 axes of rotation and 3 axes of translation) using small light-weight inertial measurement units while participants performed balance and gait tasks. Kinematic measures differed between individuals with vestibular schwannoma (at both time points) and healthy controls for the more challenging exercises, namely those performed in tandem position or on an unstable surface without visual input. Quantitative assessment of the vestibulo-ocular reflex (VOR) revealed a reduction in VOR gain for individuals with vestibular schwannoma compared to control subjects, that was further reduced following surgery. These findings indicated that the impairment caused by either the tumor or subsequent surgical tumor resection altered head-on-trunk kinematics in a manner that is not normalized by central compensation. In contrast, we further found that head-on-trunk kinematics in individuals with vestibular schwannoma were actually comparable before and after surgery. Thus, taken together, our results indicate that vestibular loss impacts head-on-trunk kinematics during voluntary balance and gait behaviors, and suggest that the neural mechanisms mediating adaptation alter the motion strategies even before surgery in a manner that may be maladaptive for long-term compensation.
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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".