Fear of Falling in People With Multiple Sclerosis: Which Clinical Characteristics Are Important?
Bibliographic record
Abstract
BACKGROUND: Fear of falling (FOF) is an important risk indicator for health-related outcomes and quality of life in patients with multiple sclerosis (MS). However, factors associated with FOF in MS are not well investigated. OBJECTIVES: This study was done to explore predictors of FOF in this population. METHODS: Seventy relapsing remitting patients with MS were evaluated. Fear of falling was assessed using the Fall Efficacy Scale-International (FES-I). Motor outcomes included: 30-second chair stand test (30s-CST), Berg Balance Scale (BBS), 10-Meter Walk Test (10MWT), and 6-Minute Walk Test (6MWT). Cognitive status was determined using the Montréal Cognitive Assessment (MOCA) and Symbol Digit Modalities Test (SDMT). Affective factors including depression, fatigue, and sleep were also assessed using the Beck Depression Inventory (BDI), Modified Fatigue Impact Scale (MFIS), and Pittsburgh Sleep Quality Index (PSQI), respectively. RESULTS: Fear of falling was significantly correlated with all motor and affective measures used. However, a stepwise regression found that only BBS from motor measures, MOCA from cognitive measures, and sleep disorders from affective factors were significantly predictive of the FOF. CONCLUSIONS: Fear of falling in patients with MS is multifactorial and includes motor and nonmotor factors. Thus, therapies that aim to reduce risk of falling in this population should address motor functions, cognitive abilities, and sleep quality.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".