Diagnosis and Management of Pediatric Chronic Hand Eczema: The PeDRA CACHES Survey
Bibliographic record
Abstract
BACKGROUND: Chronic hand eczema (CHE) significantly impacts quality of life. Published literature on pediatric CHE (P-CHE) in North America including knowledge on epidemiology and standard evaluation and management is limited. OBJECTIVE: Our objective was to assess diagnostic practices when evaluating patients with P-CHE in the US and Canada, produce data on therapeutic agent prescribing practices for the disorder, and lay the foundation for future studies. METHODS: We surveyed pediatric dermatologists to collect data on clinician and patient population demographics, diagnostic methods, therapeutic agent selection, among other statistics. From June 2021 to January 2022, a survey was distributed to members of the Pediatric Dermatology Research Alliance (PeDRA). RESULTS: Fifty PeDRA members responded stating that they would be interested in participating, and 21 surveys were completed. For patients with P-CHE, providers most often utilize the diagnoses of irritant contact dermatitis, allergic contact dermatitis, dyshidrotic hand eczema, and atopic dermatitis. Contact allergy patch testing and bacterial hand culture are the most used tests for workup. Nearly all utilize topical corticosteroids as first line therapy. Most responders report that they have treated fewer than six patients with systemic agents and prefer dupilumab as first-line systemic therapy. CONCLUSIONS: This is the first characterization of P-CHE among pediatric dermatologists in the United States and Canada. This assessment may prove useful in designing further investigations including prospective studies of P-CHE epidemiology, morphology, nomenclature, and management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".