Characterization of Tattoo Aftercare Products: Allergenic Ingredients and Marketing Claims
Bibliographic record
Abstract
BACKGROUND: Common recommendations for tattoo aftercare to ensure proper healing include application of topical products. Little is known about tattoo aftercare products. METHODS: Tattoo aftercare products were identified from a previous study and a search on Amazon.com using the phrase "tattoo aftercare." Duplicates and products without complete ingredient lists were excluded. Marketing claims were tabulated. All ingredients were entered in Excel and grouped according to Contact Allergen Management Program categories. Comparison of ingredients to North American Contact Dermatitis Group (NACDG) screening and American Contact Dermatitis Society (ACDS) Core allergens was conducted. RESULTS: A total of 84 tattoo aftercare products from 52 distinct brands were found. Forty-eight distinctive market claims were identified; the use of "natural ingredient(s)" (42.9%) was most common. There were 4 to 28 ingredients per product (mean = 11.8 ± 5.5) with a total of 369 distinct ingredients listed. Products contained an average of 7.9 ± 3.9 ACDS Core allergens per product and 7.0 ± 3.7 NACDG allergens per product. Most common allergens included fragrance/botanicals (n = 529), vitamin E derivatives (n = 43), and vitamin B5 derivatives (n = 11). CONCLUSIONS: This review of 84 products found that tattoo aftercare products contain an average of 8 ACDS Core and 7 NACDG allergens. Clinicians should be aware of potential allergens in tattoo aftercare products.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".