Prevalence and Characterization of Pruritus in Epidermolysis Bullosa
Bibliographic record
Abstract
Qualitative data suggest that pruritus is a burdensome symptom in patients with epidermolysis bullosa (EB), but the prevalence of pruritus in children and adults with EB and factors that contribute to pruritus are unknown. The objective of the current study was to quantitatively identify and to characterize pruritus that EB patients experience using a comprehensive online questionnaire. A questionnaire was developed to evaluate pruritus in all ages and all types of EB. Questions that characterize pruritus were included and factors that aggravate symptoms were investigated. Patients from seven North American EB centers were invited to participate. One hundred forty-six of 216 questionnaires were completed (response rate 68%; 73 male, 73 female; median age 20.0 years). Using a 5-point Likert scale (1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always), itchiness was the most bothersome EB complication (mean 3.3). The average daily frequency of pruritus increased with self-reported EB severity. Pruritus was most frequent at bedtime (mean 3.8) and interfered with sleep. Factors that aggravated pruritus included healing wounds, dry skin, infected wounds, stress, heat, dryness, and humidity. Pruritus is common in individuals with EB and can be bothersome. Future studies will need to investigate the most effective treatments given to individuals with EB for pruritus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".