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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".