Impairment, disability and fatigue in multiple sclerosis.
Bibliographic record
Abstract
BACKGROUND: Identifying the predictors of pain is important for both health professionals and researchers, because pain has repeatedly been found to be a strong predictor of activity limitations and participation restrictions. The objective of this study was to determine the predictors of pain presence and severity in a large, well-designed sample of community dwelling individuals with multiple sclerosis (MS). METHODS: This was a cross-sectional study. A center-stratified random sample including 188 persons with MS were recruited from three major MS clinics in the Greater Montreal, Canada. Main outcomes included pain prevalence and severity. Predictor variables included depression, anxiety, perceived health status, fatigue, sleep problems, and perceived cognitive deficits. Participants completed three questionnaires: the first asked about the socio-demographic and clinical information of the subjects, the second assessed the pain characteristics of the subjects, and the third covered the predictor variables. RESULTS: The prevalence of pain in our sample was 42%. MS- related disability was found to be in the main predictor for both pain presence and intensity. Fatigue also was a main contributor to pain presence. The results of this study also showed that pain was associated with higher levels of depression, anxiety, sleep problems, and perceived cognitive deficits, and diminished perceived health status. CONCLUSIONS: The results of this study indicated that pain is a common symptom among people with MS. Pain presence was predicted by MS-related disability and fatigue, while pain intensity was only predicted by MS severity.
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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 | 0.003 |
| 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 | 0.000 |
| 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".