Tattooing and risk for transfusion-transmitted diseases: The role of the type, number and design of the tattoos, and the conditions in which they were performed
Bibliographic record
Abstract
Tattoos have been shown to be associated with transfusion-transmitted diseases (TTDs), particularly hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. Very little is known about the association between different categories of tattoos and TTDs. In a cross-sectional study in Brazil, we studied 182 individuals with tattoos and assessed the odds of testing positive for a TTD according to tattoo type, number, design and performance conditions. Major findings were significant associations between an increasing number of tattoos and HBV infection (odds ratio (OR) of 2.04 for two tattoos and 3.48 for > or = 3 tattoos), having a non-professional tattoo and testing positive for at least one TTD (OR = 3.25), and having > or = 3 tattoos and testing positive for at least one TTD (OR = 2.98). We suggest that non-professional tattoos and number of tattoos should be assessed as potential deferral criteria in screening blood donors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".