Does Pain in Individuals with Multiple Sclerosis Affect Employment? A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with multiple sclerosis (MS) experience some of the highest unemployment rates among all groups of chronic illnesses. Pain has been found to be a common reason for sick leave or early retirement in healthy populations or other groups with chronic illness; however, there is little awareness regarding the effect of pain on the work status of individuals with MS. OBJECTIVES: To estimate the extent to which individuals with pain differ in employment status compared with those without pain among MS patients. METHODS: An extensive systematic review of the scientific literature was performed within the framework of the Cochrane Collaboration to identify studies focusing on the effect of pain on employment in individuals with MS. The following databases were searched: PubMed, EMBASE, PsychInfo, Web of Science, MD Consult and Elsevier, and Science Direct. The methodological quality of studies was assessed using the McMaster Critical Review Form. RESULTS: Ten articles met the inclusion criteria and were included in the systematic review. Of these studies, five that exhibited clinical, methodological and statistical homogeneity were included in the meta-analysis. The between-groups (pain + versus pain -) pooled random OR of being employed was 0.7 (strong), and was significantly different from unity (95% CI 0.5 to 0.9; P=0.001). CONCLUSIONS: The results of the present study indicated that individuals with MS who experience pain were significantly more likely to report a decreased employment rate than individuals with MS who were pain free.
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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 | 0.034 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".