Allergic Contact Dermatitis to Operating Room Scrubs and Disinfectants
Bibliographic record
Abstract
BACKGROUND: Both surgical personnel and patients undergoing procedures are exposed regularly to different antiseptic chemicals in various forms. Little is known about the ingredients in these antiseptics and the risk these products may provoke allergic contact dermatitis. OBJECTIVE: The aim of the study was to identify and characterize common allergens in surgical scrubs and patient surgical cleansers that health care workers and surgical patients may encounter in the perioperative period. METHODS: DailyMed website was searched using numerous terms for surgical disinfectants. Products used for health care worker handwashing/scrubbing or patient surgical cleansing/disinfecting were included. Each product's ingredients were recorded; those found on the 2017 American Contact Dermatitis Society (ACDS) Core Allergen Series were noted from each product. CONCLUSIONS: A total of 1940 products were identified, of which 267 were included in the analysis. A total of 66.3% contained iodine, 25.8% contained chlorhexidine digluconate, and 2.6% contained chloroxylenol. Within the group analyzed, 1586 ingredients were identified. Of these, 241 were ACDS Core Series allergens. Most products contained a single ACDS allergen. There were significant differences in allergens based on product type and active ingredient, with iodine-containing products having the fewest number of allergens. The most common ACDS allergens found were cocamide diethanolamide (22.5%), fragrance (21.7%), lanolin (19.5%), propylene glycol (6.7%), alkyl glucosides (6.0%), and sorbic acid derivatives (5.6%).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 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".