Loss of Work Productivity and Quality of Life in Patients With Autoimmune Bullous Dermatoses
Bibliographic record
Abstract
BACKGROUND: Little is known about quality of life and work productivity in autoimmune bullous dermatoses (AIBDs). OBJECTIVE: To determine the impact of AIBDs on quality of life and work productivity. METHODS: An observational cross-sectional study took place between February and May 2013 at an AIBD tertiary referral centre. Ninety-four patients were included. All participants completed the Dermatology Life Quality Index and the Work Productivity and Activity Impairment-Specific Health Problem questionnaires. RESULTS: Responders to treatment had less impairment (P<.001) than nonresponders. Patients with severe AIBD had significantly more impairment that those with mild (P<.001) and moderate (P=.002) AIBD. Greater impairment was associated with higher percentage of work missed. Those with a higher Dermatology Life Quality Index score had greater work impairment and overall activity impairment (P=.041, P=.024). Nonresponders had increased impairment while working (P<.001), overall work impairment (P<.001), and activity impairment (P<.001). Severely affected patients had worse impairment in all Work Productivity and Activity Impairment Questionnaire domains. CONCLUSIONS: AIBD has the potential to be a large burden on ability to work and quality of life. Larger studies are needed to clarify how these domains change over time and whether or not they improve with treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".