Understanding Falls in Multiple Sclerosis: Association of Mobility Status, Concerns About Falling, and Accumulated Impairments
Bibliographic record
Abstract
BACKGROUND: Falls in people with multiple sclerosis (MS) are a serious health concern, and the percentage of people who restrict their activity because of concerns about falling (CAF) is not known. Mobility function and accumulated impairments are associated with fall risk in older adults but not in people with stroke and have not been studied in people with MS. OBJECTIVE: The purposes of this study were: (1) to estimate the percentage of people who have MS and report falling, CAF, and activity restrictions related to CAF; (2) to examine associations of these factors with fall status; and (3) to explore associations of fall status with mobility function and number of accumulated impairments. DESIGN: A cross-sectional survey was conducted. METHODS: A total of 575 community-dwelling people with MS provided information about sociodemographics, falls, CAF, activity restrictions related to CAF, mobility function, and accumulated impairments. Chi-square statistics were used to explore associations among these factors. RESULTS: In all participants, about 62% reported CAF and about 67% reported activity restrictions related to CAF. In participants who did not experience falls, 25.9% reported CAF and 27.7% reported activity restrictions related to CAF. Mobility function was associated with fall status; participants reporting moderate mobility restrictions reported the highest percentage of falls, and participants who were nonwalkers (ie, had severely limited self-mobility) reported the lowest percentage. Falls were associated with accumulated impairments; the participants who reported the highest percentage of 2 or more falls were those with 10 impairments. LIMITATIONS: This cross-sectional study relied on self-reported falls, mobility, and impairment status, which were not objectively verified. CONCLUSIONS: Both CAF and activity restrictions related to CAF were common in people with MS and were reported by people who experienced falls and those who did not. The association of fall status with mobility function did not appear to be linear. Fall risk increased with declining mobility function; however, at a certain threshold, further declines in mobility function were associated with fewer falls, possibly because of reduced fall risk exposure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".