Pain and Itch Are Dual Burdens in Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Despite being widely reported by patients with atopic dermatitis (AD), pain symptoms, unlike itch, have not been widely assessed. OBJECTIVE: The aim of the study was to understand the distinct pain symptoms in patients with AD. METHODS: Responses from an anonymous questionnaire were collected from our eczema clinic (in-person survey) and collaboration with Global Parents for Eczema Research Group and the National Eczema Association (online survey) to assess skin pain among patients with AD 5 years and older. Eczema Area and Severity Index was measured in the clinic cohort to correlate with pain symptoms. CONCLUSIONS: In our international cohort of 103 patients with AD, 78% reported concomitant pain and itch. The greatest pain burden occurred on the hands (odds ratio [OR], 0.77), perioral region (OR, 0.74), and toes (OR, 0.7), corresponding to regions with the greatest sensory nerve density. Pain was most commonly described as "burning" and "stinging," particularly when lesions were red, cracked, and dry. Its presence significantly interfered with sleep, leisure activities, and activities of daily living. Among the clinic cohort, we observed a strong Spearman correlation between objective Eczema Area and Severity Index score and subjective skin pain. It is imperative that clinicians understand patients' unique pain burden to best evaluate clinical severity and quality-of-life interference.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".