Pain Due to Multiple Sclerosis: Analysis of the Prevalence and Economic Burden in Canada
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a neurological disease affecting approximately 50,000 Canadians. Although studies have described overall MS costs, none have focused specifically on MS-related pain. OBJECTIVES: To estimate the prevalence of MS-related pain in Canada, the proportion of patients treated and responding to treatment for MS-related pain, and the associated economic burden. METHODS: Results were captured through physician and patient surveys. Patients were recruited through MS clinics and the MS Society. Patient-reported outcomes and resource utilization over the previous six months were collected by telephone interview. Costs were measured in 2004 Canadian dollars. The economic burden was extrapolated to the population using national demographics and prevalence. Spearman's rho assessed the relationship between cost and pain severity. RESULTS: Physicians estimated that 46% of their MS patients experienced MS-related pain, and that 35% received treatment for pain. Pain was reported to be relieved somewhat in 29%+/-10% of their patients, adequately in 26%+/-19% and poorly in 27%+/-13%, while 17%+/-9% received no relief. Two hundred ninety-seven participants completed the patient survey. Seventy-one per cent (211 of 297 patients) experienced MS-related pain. Eighty per cent of patients reported taking some type of medication to manage their pain, and of these, 82% reported some reduction in pain. The mean +/- SD direct cost per patient of MS-related pain was dollars 2,528+/-5,695. The mean +/- SD indirect cost per patient was dollars 669+/-875. Total costs were positively correlated with levels of self-reported pain (rho=0.291, rho<0.0001). The estimated six-month burden of pain of MS patients in Canada was dollars 79,444,888. CONCLUSIONS: The prevalence of pain is high in MS patients. This condition may be underdiagnosed and undertreated, and results in a significant economic burden on society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".