Bibliographic record
Abstract
PURPOSE: Multiple sclerosis (MS) is an inflammatory and degenerative condition affecting the central nervous system. Like many neurologic diseases, it is chronic and incurable, and confers a substantial burden on affected individuals, their families and society. Although many individuals suffering from a serious chronic disease also suffer from comorbid conditions, the important consequences of their interaction often receive little attention. This was particularly true for MS two decades ago. Broadening our perspective by better understanding the effects of comorbidity on an individual with a particular chronic disease offers us an opportunity to improve understanding of prognosis, personalize disease management, develop new therapeutic approaches and illuminate the pathophysiology of disease. SOURCE: Studies examining the incidence, prevalence and outcomes related to comorbidity in MS will be discussed, along with areas requiring further investigation. CONCLUSION: Comorbidity is highly prevalent in MS throughout the disease course. Comorbid conditions, including depression, anxiety, hypertension, hyperlipidemia, diabetes and chronic lung disease, adversely affect a broad range of outcomes. Less is known about the effects of MS on outcomes related to these comorbid conditions. These findings highlight an urgent need to determine how to best prevent and treat comorbidity in MS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".