Bibliographic record
Abstract
BACKGROUND: Chemicals in textile manufacturing and laundering products are important sources of allergens triggering allergic contact dermatitis. Allergens corresponding to the textile production process have been well recognized. However, there is limited information regarding potential allergens in laundering products. OBJECTIVE: The aim is to investigate the presence and prevalence of potential allergens in commonly used laundering products. METHODS: An Internet-based search was performed to identify the current best-selling laundering products in the United States. Subsequent inquiry of common allergens for each product was collected through a review of ingredients listed by manufacturers. RESULTS: Sixty-five laundering products were examined: 30 laundry detergents, 10 fabric softeners, 8 dryer sheets, and 17 stain removers. Ten common allergens were identified: benzisothiazolinone, benzyl benzoate, cocamidopropyl betaine, decyl glucoside, "fragrances," lauryl glucoside, methylisothiazolinone, methylchloroisothiazolinone, phenoxyethanol, and propylene glycol. Fragrances and essential oils are the top allergens in laundry detergents (66.7%), fabric softeners (90%), dryer sheets (75%), and stain removers (58.8%). Laundry detergents labeled as "baby safe" and "free and gentle" contained common allergens, with methylisothiazolinone being the most prevalent, in 80% and 57.1%, respectively. CONCLUSIONS: Textile dermatitis can negatively impact quality of life and function. Aside from textile dyes and finishing resins, laundering products should also be considered.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".