Disperse Blue Dyes 106 and 124 are Common Causes of Textile Dermatitis and Should Serve as Screening Allergens for This Condition
Bibliographic record
Abstract
BACKGROUND: Textile dye dermatitis is frequently undiagnosed because clinical awareness is low and because of the absence of good screening allergens in standard patch test series for this type of contact dermatitis. OBJECTIVES: To determine the incidence of textile dye allergy in patients with problematic eczemas evaluated at a contact dermatitis clinic, and to determine the incidence of allergic contact dermatitis to diperse blue dyes in these patients. METHODS: We conducted a retrospective study of 788 patients who were patch tested to either the North American Contact Dermatitis Group (NACDG) Standard Series or the European Standard Series, in addition to other relevant series. The Chemotechnique textile series was utilized in 271 patients (28%). RESULTS: Forty patients reacted positively to 1 or more textile dyes, the majority reacting positively to Disperse Blue 106 (33 of 40; 82.5%) and to Disperse Blue 124 (32 of 40; 80%). Ten of 11 tested patients reacted to their own clothing, 9 of whom reacted to the blue/black 100% acetate or 100% polyester liners in their garments. CONCLUSIONS: Textile dye allergy is more common than previously reported. It can cause marked dermatitis and widespread autoeczematization reactions. The most frequent allergens are Disperse Blue 106 and 124, which are frequently found in the 100% acetate and 100% polyester liners of women's clothing. We recommend that Disperse Blue 106 or 124 serve as the screening allergen for textile dye dermatitis.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".