Tattoos and transfusion-transmitted disease risk: implications for the screening of blood donors in Brazil
Bibliographic record
Abstract
Having a tattoo has been associated with serological evidence of hepatitis B and C viruses, as well as human immunodeficiency virus infections and syphilis; all of these are known to be transmissible by blood transfusion. These associations are of higher magnitude for individuals with nonprofessionally-applied tattoos and with two or more tattoos. Tattoos are common among drug addicts and prisoners, conditions that are also associated with transfusion-transmitted diseases. We examined the implications of these associations for the screening of blood donors in Brazil. Numbers of individuals who would be correctly or unnecessarily deferred from blood donation on the basis of the presence of tattoos, and on their number and type, were calculated for different prevalence situations based on published odds ratios. If having a tattoo was made a deferral criterion, cost savings (due to a reduced need for laboratory testing and subsequent follow-up) would accrue at the expense of the deferral of appropriate donors. Restricting deferral to more at-risk sub-groups of tattooed individuals would correctly defer less individuals and would also reduce the numbers of potential donors unnecessarily deferred. Key factors in balancing cost savings and unnecessary deferrals include the magnitude of the pool of blood donors in the population, the prevalence of individuals with tattoos and the culture of tattoos in the population. Tattoos can therefore be an efficient criterion for the screening of blood donors in certain settings, a finding that requires corroboration from larger population-based studies.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".