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Record W2997780802 · doi:10.1097/der.0000000000000566

Contact Allergens in Top-Selling Textile-care Products

2020· article· en· W2997780802 on OpenAlexvenueno aff
Heidi Bai, Idy Tam, JiaDe Yu

Bibliographic record

VenueDermatitis · 2020
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTextileComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.245
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2020
Admission routes1
Has abstractyes

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