The Impact of Systemic Lupus Erythematosus on Employment
Bibliographic record
Abstract
OBJECTIVE: Our primary objective was to examine work status (e.g., job loss, changes in amount worked) and predictors of job loss in patients with systemic lupus erythematosus (SLE). METHODS: Recently diagnosed SLE patients were enrolled in the Carolina Lupus Study between 1997 and 1999; an age-, sex-, and state-matched control group selected through driver's license registries for the 60-county study area was also enrolled. In 2001, a followup study of both groups was conducted (median 4 yrs since diagnosis). Work history data were obtained in an in-person interview at enrollment and a telephone interview at followup. RESULTS: Fifty-one patients (26%) and 26 controls (9%) (p < 0.0001) who were working the year before diagnosis (or for controls, a corresponding reference year) were no longer working at followup; 92% of patients compared with 40% of controls who were no longer working indicated that they had stopped working because of their health (p < 0.0001). College graduates were less likely to quit their jobs due to health compared to non-college graduates (adjusted OR = 0.27, 95% CI 0.09, 0.84). SLE patients with arthritis were 3 times more likely to have left their jobs due to health reasons compared to those who didn't have arthritis (adjusted OR = 3.3, 95% CI 1.2, 8.8); an association was also seen with pleuritis (adjusted OR 2.3, 95% CI 1.1, 4.6). CONCLUSION: The burden expressed as work cessation due to health, especially among lesser educated patients and those with arthritis or pleuritis, is significant even early in the disease process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".