Vestibular and Motor Contributions to Mobility: Limitations of Seniors Awaiting Discharge from Hospital Care
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Following hospitalization, seniors are at risk of impaired mobility and increased risk of falling, which can lead to injuries and re-admission. The primary purpose of this paper was to evaluate the ability of hospitalized seniors to use vestibular inputs for balance control. The secondary purpose was to examine the influence of vestibular function and lower limb muscle strength on mobility. METHODS: Experimental and correlation designs were used. Patients (aged 65-90 years), preparing for discharge from an inpatient geriatric rehabilitation unit, were recruited. Vestibular control of standing balance was measured using the Clinical Test of Sensory Interaction for Balance (CTSIB). Mobility was measured with the Timed Up and Go (TUG) Test. Lower limb muscle maximum voluntary isometric contraction (MVIC) strength was tested with portable dynamometry. Wilcoxon signed rank test, with alpha adjusted for multiple comparisons (p ≤ 0.017), was used to compare relevant components of the CTSIB. Stepwise regression was used to assess the influence of vestibular impairment on TUG score. RESULTS: CTSIB(Test6) (median = 7.1 seconds, range = 0.0-30.0) was less than CTSIB(Test1) (30.0 seconds, 30.0-30.0) and CTSIB(Test4) (30.0 seconds, 10.5-30.0) (W = 136, p < 0.017). MVIC scores (Nm·kg⁻¹, mean ± SD) included hip abduction 0.38 ± 0.2, hip flexion 0.32 ± 0.1, hip extension 0.44 ± 0.2, knee flexion 0.31 ± 0.1, knee extension 0.33 ± 0.2, ankle dorsiflexion 0.12 ± 0.1 and ankle plantarflexion 0.23 ± 0.1. Mean TUG score was 26.1 ± 6.0 seconds. Performance on CTSIB(Test6) explained 55% of the variance in TUG scores, whereas hip extension strength explained an additional 6%. CONCLUSIONS: Seniors awaiting discharge from hospital had impaired vestibular control of balance that was systematically associated with impaired mobility. Evaluating vestibular function prior to discharge from hospital could improve discharge planning with respect to management of impairments that threaten balance and safe mobility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".