Frailty Exacerbates Disability in Progressive Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: To evaluate frailty in severe progressive multiple sclerosis (PMS) and to investigate the underlying mechanisms. METHODS: This prospective, cross-sectional, multicenter study enrolled a late severe PMS group requiring skilled nursing (n = 53) and an age, sex, and disease duration-matched control PMS group (n = 53). Participants received neurological and MRI assessments and provided blood samples. Frailty was measured on the Edmonton Frail Scale. Disability was measured on the Expanded Disability Status Scale (EDSS), and fatigue was assessed on the Fatigue Severity Scale. The inflammatory vulnerability index (IVX) and metabolic vulnerability index (MVX) were computed from nuclear magnetic resonance spectroscopy-derived metabolomic profiling. Serum neurofilament (sNfL), glial fibrillary acidic protein (GFAP), and growth differentiation factor 15 (GDF15) levels were obtained. RESULTS: The late severe PMS group had a higher median EDSS (8.0 vs. 6.0, p < 0.001) than the matched control PMS group. The late severe PMS group had a higher prevalence of frailty (73.1% vs. 23.1%, p < 0.001) and higher frailty scores (8.87 vs. 5.52, p < 0.001) than the control PMS group. EFS was associated with EDSS in both PMS groups. Positive frailty status was associated with a 1.19-point greater EDSS (p = 0.012) in the control PMS group and a 0.436-point greater EDSS in the late severe PMS group (p = 0.002). In PMS controls, the EFS and frailty status were associated with IVX (p = 0.044 for EFS) and MVX (p = 0.036 for EFS). CONCLUSIONS: Frailty is positively associated with MS disability. Inflammatory and metabolic vulnerability are associated with frailty in PMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".