Relationship of Balance and Mobility to Fall Incidence in People With Chronic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: People with stroke are at risk for falls. The purpose of this study was to estimate the strength of the relationship of balance and mobility to falls. SUBJECTS: The participants were 99 community-dwelling people with chronic stroke. METHODS: An interview was used to record fall history, and physical performance assessments were used to measure balance (Berg Balance Scale [BBS]) and mobility (gait speed). RESULTS: No differences were found between subjects who fell once and subjects who did not fall or between subjects who fell more than once and subjects who did not fall. Neither balance nor mobility was able to explain falls in people with chronic stroke. DISCUSSION AND CONCLUSION: Clinicians should be cautious when using the BBS or gait speed to determine fall risk in this population. Falls occurred frequently during walking; it may be necessary to focus on reactive balance and environmental interaction when assessing individuals for risk of falls and devising fall prevention programs for individuals with chronic stroke. The authors' observations suggest that the prescription of 4-wheel walkers for individuals with a low BBS score (</=45) may be a mobility aid that could reduce the risk of falls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 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".