Head movement kinematics are differentially altered for extended versus short duration gait exercises in individuals with vestibular loss
Bibliographic record
Abstract
Head kinematics are altered in individuals with vestibular schwannoma (VS) during short duration gait tasks [i.e., Functional Gait Assessment (FGA)], both before and after surgery, yet whether these differences extend to longer duration gait exercises is currently unknown. Here we examined the effects of vestibular loss and subsequent compensation on head kinematics in individuals with VS during gait exercises of relatively extended versus short duration (< 10 versus 30 s), compared to age-matched controls. Six-dimensional head movements were recorded during extended and short duration gait exercises before and then 6 weeks after sectioning of the involved vestibular nerve (vestibular neurectomy). Standard functional, physiological, and subjective clinical assessments were also performed at each time point. Kinematics were differentially altered in individuals with vestibular loss at both time points during extended versus short duration exercises. Range of motion was significantly reduced in extended tasks. In contrast, movement variability predominately differed for the short duration exercises. Overall, our results indicate that quantifying head kinematics during longer duration gait tasks can provide novel information about how VS individuals compensate for vestibular loss, and suggest that measurements of range of motion versus variability can provide information regarding the different strategies deployed to maintain functional locomotion.
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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".