Drawing on Related Knowledge to Advance Multiple Sclerosis Falls-Prevention Research
Bibliographic record
Abstract
There is much to be learned from falls-related research outside the field of multiple sclerosis (MS), as well as from work within the MS field but not specific to falls or falls prevention. This article describes three examples of such bodies of work that have potential to broaden approaches to falls-prevention research: 1) sensory components of postural control among older adults, 2) lessons learned from physical activity promotion among people with spinal cord injury (SCI), and 3) aging among people with MS. Age-related deterioration in visual, vestibular, and somatosensory systems or in sensory integration can adversely affect postural control and can contribute to falls in older people. Sensory-specific interventions designed for improving balance in older people could be adapted for preventing falls in individuals with MS. Spinal Cord Injury (SCI) Action Canada's strategy for disseminating physical activity promotion interventions for adults with SCI has been successful and widely accepted by community partners. Many of the peer-based interventions developed by SCI Action Canada are potentially relevant and could be adapted to the MS population for both physical activity promotion and falls prevention. Considering that older people with MS constitute a growing proportion of the MS population and over 70% of older people with MS report moderate to extreme balance problems, falls prevention should be one of the key components, particularly for MS management in older or more disabled groups. Overall, given people's different ages, symptoms, strengths, and barriers, a tailored MS falls-prevention intervention that includes peer/caregiver support is critical.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".