Tattoos as risk factors for transfusion-transmitted diseases
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: 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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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