Longitudinal Evaluation of Falls and Fall Prevention Strategies in Multiple Sclerosis (P1.130)
Bibliographic record
Abstract
OBJECTIVE: To assess changes in falls and fall prevention strategies in people with multiple sclerosis (MS) over 2 years. BACKGROUND: People with MS fall frequently and use various strategies to manage fall risk. Changes in fall incidence and use of fall prevention strategies over time are not well understood. DESIGN/METHODS: 58 community-dwelling people with MS aged 18-50 with EDSS ≤ 6.0 were followed for 24 months. The number of falls in the previous 12 months, fall prevention strategy use (fall prevention strategy survey (FPSS) responses), and demographics, were reported at baseline, 12 months and 24 months. Changes in the proportion of recurrent fallers (蠅 2 falls in the prior 12 months) and use of fall prevention strategies were assessed. RESULTS: At baseline, subjects’ average age was 40 years old and their average EDSS score was 2.7. 70[percnt] were female and 95[percnt] had relapsing remitting MS. The proportions of recurrent fallers did not change significantly over time (baseline and 12 months: 62[percnt]; 24 months: 57[percnt]; Cochran’s Q test, p=0.53). FPSS scores and the number of fall prevention strategies used did change significantly over time (median scores/number at baseline: 5/5, 12 months: 6 (p=0.04)/6 (p<0.01), 24 months: 7 (p=0.02)/5.5 (p=0.04)). The type and specific strategies used also changed over time. At all time points, turning on lights was the most popular strategy and talking to health care professionals about fall prevention and medications, the most challenging behaviors, were the least used strategies. However, over time, more people avoided high risk activities, monitored and managed their MS symptoms, and asked other people for help, to prevent falls. CONCLUSIONS: Many people with MS fall recurrently. The strategies they use to prevent falls change over time suggesting they may be taking action in response to their fall experiences and thus receptive to fall prevention interventions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
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".