Pain in Portuguese Patients with Multiple Sclerosis
Bibliographic record
Abstract
Early reports often ignored pain as an important symptom in multiple sclerosis (MS). Pain prevalence figures in MS from European countries other than Portugal range between 40 and 65%. To our knowledge there is no published data in English on pain in MS in Portugal. We describe the demographic and clinical characteristics, with an emphasis on pain, of 85 MS patients followed-up in a Portuguese hospital, contributing to pain epidemiology in MS. Patients were interviewed sequentially after their regular appointments at the MS clinic; patients with pain completed The Brief Pain Inventory and The McGill Pain Questionnaire (MPQ). The prevalence of pain found was 34%. Headache and back pain were the most common anatomical sites described, followed by upper and lower limbs. Intensity of pain in an 11-point scale was, for the maximum pain intensity 6.7 ± 1.8, for the minimum pain intensity 2.2 ± 2.0, for the mean pain intensity 4.5 ± 1.5, and for the actual pain intensity 2.4 ± 2.9. Pain interfered significantly with general activity, mood, work, social relations, and enjoyment of life. All MS patients with pain employed words from both the sensory and affective categories of the MPQ to describe it. Patient pain descriptions' included the word "hot-burning" in 59% of the cases, common in the report of central pain, but neuropathic pain medications were only used by 10% of them. Pain is an important symptom in Portuguese patients with MS, not only because of the high prevalence found, concordant with other European countries, but also because of its interference with quality-of-life.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".