Prevalence, Correlates, and Viral Dynamics of Hepatitis Delta among Injection Drug Users
Bibliographic record
Abstract
BACKGROUND: Most hepatitis delta virus (HDV) prevalence estimates from the United States are >10 years old, and HDV has shown significant temporal variation in other populations. HDV-hepatitis B virus (HBV) dual infection progresses rapidly, has more complications, and has a different treatment regimen than HBV infection alone. Accurate estimates of prevalence and risk factors are important to help clinicians decide who to screen. METHODS: Injection drug users in Baltimore, Maryland, who were positive for HBV serologic markers were tested for hepatitis delta antibody (HDAb) at 2 time periods: 1988-1989 (194 participants) and 2005-2006 (258 participants). Those who were HDAb positive in 2005-2006, plus a random sample of HDAb negative, HBV-positive participants were tested for HDV RNA, HBV DNA, and HCV RNA. Characteristics associated with HDV exposure and viremia were identified. RESULTS: HDV prevalence declined from 15% in 1988-1989 to 11% in 2005-2006. Among those with chronic HBV infection, prevalence increased from 29% (14 of 48 participants) to 50% (19 of 38 participants) (P=.05). Visiting a "shooting gallery" (a location where people gather to inject illegal drugs) was a strong correlate of HDAb positivity (relative risk, 3.08; P=.01). Eight (32%) of those who were HDAb positive had HDV viremia. Viremic participants had elevated liver enzyme levels and more emergency room visits. CONCLUSIONS: The temporal increase in HDV prevalence among those with chronic HBV infection is troubling; understanding this change should be a priority to prevent the burden from increasing.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".