Tattoos as risk factors for transfusion-transmitted diseases
Bibliographic record
Abstract
BACKGROUND: Several infectious diseases have been found to be associated with tattooing, including some transfusion-transmitted diseases (TTDs). Information on tattooing has been included in screening interviews of prospective blood donors and may be a reason for deferral. METHODS: Review of articles identified through Medline (and other computerized data bases) using medical subject heading (MeSH) terms and textwords for "tattooing," "transfusion", "hepatitis", "human immunodeficiency virus", "acquired immunodeficiency syndrome", "syphilis", "Chagas disease", "infection", "risk factors", and their combinations. RESULTS: There is strong evidence for the transmission of hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection, and syphilis by tattooing. Tattooing may also transmit the human immunodeficiency virus (HIV), although convincing evidence is still lacking. There is little or no evidence that other TTDs can be transmitted by tattooing. Epidemiologic studies to date have shown a large variation in odds ratio estimates of the association between tattooing and HBV, HCV, and HIV infections. CONCLUSION: Further studies are required to clarify the risk of tattoos in transmitting infectious diseases through blood transfusions. A reassessment of tattoos as a screening criterion among blood donors is justified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".