Assessment of work limitations and disability in systemic vasculitis.
Bibliographic record
Abstract
OBJECTIVES: Despite advances in the management of systemic vasculitis (SV), direct consequences of the disease, leading to impairments in physical and mental function can cause disability. The objective of this study was to assess work limitations in SV. METHODS: SV patients were recruited from a tertiary care clinic. Work disabled (WD) was defined as not working, early retirement, or reduced hours at work. Participants who were working at the time of enrolment completed the Work Limitations Questionnaire (WLQ). Other work-related measures were self-reported by questionnaire. Disease outcome measures (Vasculitis Damage Index (VDI), Health Assessment Questionnaire-Disability Index (HAQ) and pain visual analogue score (VAS)) were obtained at time of WLQ. RESULTS: 103 participants were enrolled with mean age 58 (SD17), 60% females, 48% with anti-neutrophilic cytoplasmic antibody-associated vasculitis (AAV), 26% with large vessel vasculitis (LVV) and 26% with other types of SV. 22 (21%) were WD secondary to SV, 29 (28%) were working and 52 (51%) subjects were not working for reasons other than SV. SV-related WD subjects were more likely to have a lower level of education (p=0.003) than non-WD subjects. The VDI was higher in SV-related WD vs. non-WD subjects: 1.9 (SD 2.7) vs. 2.9 (SD 1.4); p=0.015. 38 subjects were working in some capacity and completed the WLQ; their productivity loss was 8.2% and this was highly correlated with HAQ and pain VAS (rho=0.585 and rho=0.458, respectively). CONCLUSIONS: SV-related work disability occurred in 21% and was associated with lower levels of education, higher disease severity and worse functional outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".