Longitudinal evaluation of injurious falls and fall prevention strategy use among people with multiple sclerosis
Bibliographic record
Abstract
Falls among people with multiple sclerosis (MS) are often injurious. We conducted a prospective cohort study using data collectedat baseline, 12 months and 24 months to investigate the prevalence of self-reported injurious falls and trends in fall preventionstrategy use among people with MS over this period. Fifty-eight community-dwelling people with MS between the ages of18 and 50 years, with Expanded Disability Status Scale (EDSS) scores < 6.0, were recruited. Measures included self reportedinjurious falls in the past year and scores on the Fall Prevention Strategies Survey (FPSS). A total of 43 subjects completed thestudy. Prevalence of self-reported injurious falls was 40%, 35%, and 16% respectively at each time point. Seventy-one percent ofsubjects reporting injurious falls at baseline (12/17) also reported injurious falls at 12 and/or 24 months. Subjects were dividedinto three subgroups for further analysis: subjects reporting injurious falls at baseline (N = 17); subjects reporting no injuriousfalls at baseline but subsequent injurious falls (N = 8), and subjects reporting no injurious falls over the 24-months (N = 18). That analysis revealed variations in injurious fall experiences and fall prevention strategy use by subgroup. FPSS scores for eachsubgroup improved at 24-months compared to baseline. Subgroup analyses yielded insights into sources of variation in injuriousfall rates. Findings point to the potential value of using: a) self-reported history of injurious falls to predict future injuriousfalls; and b) brief interventions to motivate engagement in fall prevention behaviors. Additional studies are needed to test thesehypotheses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".