Potential Allergens in Wound Care Products
Bibliographic record
Abstract
Background: Patients with chronic wounds have an increased risk of developing allergic contact dermatitis (ACD). Reports of ACD to wound care products are not uncommon. To minimize contact sensitization in patients with chronic wounds, allergenic ingredients should be avoided when possible. Objective: With more than 5000 wound care products available in the United States, it is essential to understand which products can be chosen to minimize allergen exposures. Methods: Ingredients in wound care products in 5 wound care clinics across 2 institutions were cross-referenced with the American Contact Dermatitis Society core allergen series 2020. Results: Of the 267 wound care products included, 97 (36.3%) contained at least one allergen, including 31 dressings/wraps (22.3%), 25 medications (69.4%), 12 cleaning supplies (36.3%), 16 tapes/glues (80%), 2 instruments (14.3%), 8 emollients and vehicles (61.5%), 1 ostomy product (11.1%), and 2 odor-eliminating products (66.7%). Thirty-four different allergens were identified across all products. The most common allergens present in the included items were acrylates and propylene glycol, followed by parabens, cetyl stearyl alcohol, tocopherol, fragrance, and phenoxyethanol. Conclusions: Many wound care products contain at least one contact allergen, highlighting the importance of clinician education on ACD in the context of wound care product selection.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".