Impact of aging on treatment considerations for multiple sclerosis patients
Bibliographic record
Abstract
With a rapidly aging global population and improvement of outcomes with newer multiple sclerosis (MS)-specific disease-modifying therapies (DMTs), the epidemiology of MS has shifted to an older than previously described population, with a peak prevalence of the disease seen in the 55-65 years age group. Changes in the pathophysiology of MS appear to be age-dependent. Several studies have identified a consistent phase of disability worsening around the fifth decade of life. The latter appears to be independent of prior disease duration and inflammatory activity and concomitant to pathological changes from acute focal active demyelination to chronic smoldering plaques, slow-expanding lesions, and compartmentalized inflammation within the central nervous system (CNS). On the other hand, decreased CNS tissue reserve and poorer remyelinating capacity with aging lead to loss of relapse recovery potential. Aging with MS may imply longer exposure to DMTs, although treatment efficacy in patients >55 years has not been evaluated in pivotal randomized controlled trials and appears to decrease with age. Older individuals are more prone to adverse effects of DMTs, an important aspect of treatment individualization. Aging with MS also implies a higher global burden of comorbid illnesses that contribute to overall impairments and represent a crucial confounder in interpreting clinical worsening. Discontinuation of DMTs after age 55, when no evidence of clinical or radiological activity is detected, is currently under the spotlight. In this review, we will discuss the impact of aging on MS pathobiology, the effect of comorbidities and other confounders on clinical worsening, and focus on current therapeutic considerations in this age group.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".