Glamour, expression, and consequences of tattoos in radiation treatment
Bibliographic record
Abstract
It is estimated that approximately 24% of the US population has at least one tattoo. However, tattoo ink ingredients include heavy metals (high atomic number Z) that are not regulated, which can cause skin reactions. This study investigates the dosimetric effects in surface dose due to high-Z elements in tattoo ink under electron beam irradiation. Four commercially available tattoo ink colors, black, red, yellow, and blue were chosen. The elemental composition of the tattoo ink samples was analyzed using X-ray Fluorescence (XRF). An ultrathin-window parallel plate ion chamber was used to measure the surface dose perturbation (ratio of ionizations with and without tattoo ink) for 6 - 20 MeV electron beams. The elemental concentration in the tattoo ink samples showed high-Z elements, with Z ranging from 11 to 92. The dose perturbation ranged from 1.4% up to 6% for the yellow ink for the 6 MeV electron beam, with similar values across the rest of the electron energies, whereas the black, red, and blue inks presented up to 3% dose perturbation for the same range of energies. Based on this initial study, we conclude that commercially available tattoo inks contain large amounts of high-Z metals that may contribute to dose perturbation. Therefore treatment of superficial lesions with electron beams in a tattooed area should be monitored for signs of early skin reaction during radiation therapy treatments.
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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 | 0.000 |
| 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) | 0.002 | 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".