Impact of vasculitis on employment and income.
Bibliographic record
Abstract
OBJECTIVES: Work disability associated with rheumatic diseases accounts for a substantial financial burden. However, few studies have investigated disability among patients with vasculitis. The purpose of this study was to examine the impact of vasculitis on patient employment and income. METHODS: Patients enrolled in the Vasculitis Clinical Research Consortium (VCRC) Patient Contact Registry, living in the USA or Canada, and followed for >1 year post-diagnosis, participated in an online survey-based study. RESULTS: 421 patients with different systemic vasculitides completed the survey between June and December 2015. The majority of patients were female (70%) and Caucasian (90%); granulomatosis with polyangiitis (GPA) was the most common type of vasculitis (49%), and the mean age at the time of diagnosis was 53 years. At the time of their diagnosis of vasculitis 76% of patients were working a paid job, 6% were retired, and 2% were on disability. Over the course of their disease, and with a mean follow-up of 8±6.4 years post-diagnosis, 26% of participants became permanently work disabled or had to retire early due to vasculitis. Variables that were independently associated with permanent work disability included work physicality, less supportive work environment, and symptoms such as respiratory disease, pain, and cognitive impairment. Overall, patients reported a mean productivity loss of 6.9% and income was reduced by a median of 45%. CONCLUSIONS: Due to their vasculitis, patients frequently suffer substantial limitations in work and productivity, and personal income 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".