Are Scores on Balance Screening Tests Associated with Mobility in Older Adults?
Bibliographic record
Abstract
PURPOSE: To examine associations between measures of static and dynamic balance and performance of mobility tasks in older adults. METHODS: A cross-sectional analysis from 95 community dwelling participants (mean age 80.9 years, range 65 -03 years). Participants performed tests of static (tandem stance) and dynamic (360 degrees turn) balance and mobility (walking speed and timed chair rise). Associations among balance and mobility measures were examined using correlation and logistic regression. RESULTS: Static and dynamic balance were moderately associated (r = -.462). Relationships between dynamic balance and mobility were stronger than those between static balance and mobility. The association between dynamic balance and walking speed was particularly strong (r = -.701 ). Using logistic regression, age, and balance performance were significant predictors for outcomes of walking speed (dichotomized to < .0 m/s, >/= .0 m/s), and timed chair rise (dichotomized to </= 3.6 s, > 3.6 s). Faster 360 degrees turn times were independently associated with faster walking speed and chair rise time. CONCLUSION: Mobility tasks require both dynamic and static balance. As falls are a major health risk for older adults, including brief assessments of dynamic and static balance in the examination of older adults provides valuable information about physical function and 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".