Characterization of Tattoo Aftercare Products: Allergenic Ingredients and Marketing Claims
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it