Severity of chronic pain and its relationship to quality of life in multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: This study used reliable and validated instruments to compare pain severity in multiple sclerosis (MS) to that in other chronic painful conditions, and to examine relationships between chronic pain in MS and health-related quality of life (HRQOL). METHODS: Ninety-nine MS patients completed a self-administered survey comprised of the Medical Outcomes 36-Item Short-Form Health Survey, the Short-Form McGill Pain Questionnaire, and the Hospital Anxiety and Depression Scale. RESULTS: Pain severity was not different between MS patients with pain and rheumatoid arthritis (P = 0.77) or osteoarthritis (P = 0.98) patients. Chronic pain in MS was less often neurogenic than non-neurogenic, although severity of neurogenic pain was greater than that of non-neurogenic pain (P = 0.048). Chronic pain in MS was found to have no significant relationship to age, disease duration or disease course. Instead, we found that pain was correlated with aspects of HRQOL, particularly mental health (r = 0.44, P < 0.0001) versus physical functioning (r = 0.19, P > 0.05). Chronic pain was significantly related to anxiety and depression for females but not for males with MS. CONCLUSIONS: Chronic pain in MS is as severe as pain in arthritic conditions and is associated with reduced HRQOL. Thus, pain can be a significant symptom for MS patients and the need for treatment may be underestimated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".