Tattooing and the risk of transmission of hepatitis C: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: In this systematic literature review we sought to determine whether tattooing is a risk factor for the transmission of hepatitis C. METHODS: A comprehensive search was performed to identify all case-control, cohort or cross sectional studies published prior to November 2008 that evaluated risks related to tattooing or risk factors of transmission of hepatitis C infection. RESULTS: A total of 124 studies were included in this systematic review, of which 83 were included in the meta-analysis. The pooled odds ratio (OR) and 95% confidence interval (CI) of the association of tattooing and hepatitis C from all studies was 2.74 (2.38-3.15). In a subgroup analysis we found the strongest association between tattooing and risk of hepatitis C for samples derived from non-injection drug users (OR 5.74, 95% CI 1.98-16.66). CONCLUSIONS: Findings from the current meta-analysis indicate that tattooing is associated with a higher risk of hepatitis C infection. Because tattooing is more common among the youth and young adults and hepatitis C is very common in the imprisoned population, prevention programs must focus on youngsters and prisoners to lower the spread of hepatitis infection.
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.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".