Frailty in ageing persons with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Recent progress in multiple sclerosis (MS) management has contributed to a greater life expectancy in persons with MS. Ageing with MS comes with unique challenges and bears the potential to greatly affect quality of life and socioeconomic burden. OBJECTIVES: To compare frailty in ageing persons with multiple sclerosis (pwMS) and controls; to correlate frailty with MS clinical characteristics. METHODS: PwMS and controls over 50 years old were recruited in a cross-sectional study. Two validated frailty measures were assessed: the frailty index and the Fried's phenotype. Several multiple linear regressions accounting for demographic and clinical characteristics were performed. RESULTS: Eighty pwMS (57 females, mean age 58.5 ± 6 years old) and 37 controls (24 females, mean age 61 ± 6.5 years old) were recruited. Multivariable analysis identified significantly higher frailty index in pwMS (0.21 ± 0.12 vs 0.11 ± 0.08, p < 0.0001). Similarly, according to Fried's phenotype, a significantly higher percentage of pwMS were frail compared to controls (28% vs 8%). In pwMS, frailty index was independently associated with expanded disability status scale (EDSS), comorbidities, education level and disease duration. CONCLUSION: Our results suggest that frailty can be routinely assessed in pwMS. Increased frailty in MS patients suggests that, along with MS therapeutics, a tailored multidisciplinary approach of ageing pwMS is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".