Allergenic Ingredients in Health Care Hand Sanitizers in the United States
Bibliographic record
Abstract
BACKGROUND: Health care workers with occupational contact dermatitis often attribute their symptoms to frequent use of alcohol-based hand sanitizers. However, ingredient lists are difficult to obtain, and safe alternatives typically must accommodate brands utilized by a particular hospital system. OBJECTIVE: The aims of this study were to investigate allergenic ingredients present within health care hand sanitizers and to provide a comprehensive product list to assist with allergen avoidance. METHODS: Five major hospitals in Minnesota and 20 hospitals across the United States were called to obtain a product list. The National Library of Medicine's DailyMed Web site was searched to retrieve ingredients. Ingredients were compared with the American Contact Dermatitis Society 2017 Core Allergen Series and cross-reactors. RESULTS: The most common brands included Purell, Ecolab, DebMed, and Avagard. Active ingredients consisted of ethyl alcohol (85.0%), benzalkonium chloride (8.8%), or isopropyl alcohol (2.5%). Top 5 allergens included tocopherol (51.3%), fragrance (40.0%), propylene glycol (27.5%), benzoates (25.0%), and cetyl stearyl alcohol (12.5%). Four sanitizers were free of all American Contact Dermatitis Society allergens; 15 products contained only tocopherol or propylene glycol as allergens. CONCLUSIONS: We identified 19 low-allergen hand sanitizers within the most common brands utilized by US hospital systems. This product list will be useful for patients and health care workers seeking allergen avoidance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".