Comorbidity is associated with pain-related activity limitations in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Comorbidities are common in multiple sclerosis (MS). The high prevalence of pain in MS is well-established but the influence of comorbidities on pain, specifically, pain-related interference in activity is not. OBJECTIVE: To examine the relationship between comorbidity and pain in MS. METHODS: We recruited 949 consecutive patients with definite MS from four Canadian centres. Participants completed the Health Utilities Index (HUI-Mark III) and a validated comorbidity questionnaire at 3 visits over 2 years. The HUI's pain scale was dichotomized into two groups: those with/without pain that disrupts normal activities. We used logistic regression to assess the association of pain with each comorbidity individually at baseline and over time. RESULTS: The incidence of disruptive pain over two years was 31.1 per 100 persons. Fibromyalgia, rheumatoid arthritis, irritable bowel syndrome, migraine, chronic lung disease, depression, anxiety, hypertension, and hypercholesterolemia were associated with disruptive pain (p<0.006). Individual-level effects on the presence of worsening pain were seen for chronic obstructive pulmonary disease (odds ratio [OR]: 1.50 95% CI: 1.08-2.09), anxiety (OR: 1.49 95% CI: 1.07-2.08), and autoimmune thyroid disease (OR: 1.40 95% CI: 1.00-1.97). CONCLUSION: Comorbidity is associated with pain in persons with MS. Closer examination of these associations may provide guidance for better management of this disabling symptom 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".