Employment and work disability in systemic lupus erythematosus: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Many studies have provided information on employment and work disability (WD) rates in patients with SLE, yet are often limited by small sample sizes, poor generalizability or fail to examine the risks and outcomes of WD. Our objective was to systematically review the literature on WD in SLE to identify a more generalizable point estimate and range of WD in SLE patients. METHODS: A search was conducted using Medline, EMBase, PubMed and Cochrane databases to identify publications related to SLE and employment and/or WD. Characteristics of the study samples and employment/WD data were extracted. Descriptive statistics, a test for heterogeneity and random effects models were performed to obtain pooled estimates of employment and WD rates for all patients. RESULTS: Twenty-six studies with a total of 9886 SLE patients were found; however, not all patients were reviewed for WD. Larger studies demonstrated the prevalence of WD at 20-40%, and pooled estimates found that 46% (95% CI 40%, 52%) were employed with SLE and 34% (95% CI 24%, 44%) had WD. WD was related to psychosocial and disease-related factors including age, race, socioeconomic status (SES), education, disease activity and duration, pain, fatigue, anxiety and neurocognitive involvement. CONCLUSIONS: This study provides strong evidence that costs of SLE may be very high due to job loss at a younger age in SLE patients, and identifies some risk factors associated with WD, which should be targeted by interventions aimed at preventing job loss.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".