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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".