Prevalence and Impact of Hepatitis E Virus Infection Among Persons With Chronic Hepatitis B Living in the US and Canada
Bibliographic record
Abstract
BACKGROUND: Patients with chronic hepatitis B virus (HBV) may experience spontaneous biochemical flares of liver disease activity. This study aimed to determine (i) the prevalence of prior and possible acute hepatitis E virus (HEV) infection among persons with chronic HBV and (ii) whether HEV infection is associated with liver disease flares among persons with chronic HBV. METHODS: Serum from a random sample of 600 adults in the Hepatitis B Research Network Cohort Study was tested for HEV RNA and anti-HEV IgM and IgG. Logistic regression models were used to estimate crude and adjusted odds ratios of anti-HEV prevalence for participant characteristics. RESULTS: Anti-HEV IgG and IgM seroprevalence was 28.5% and 1.7%, respectively. No participants had detectable HEV RNA. Of the 10 anti-HEV IgM+ participants, only 1 had elevated serum ALT at seroconversion. The odds of anti-HEV seropositivity (IgG+ or IgM+) were higher in older participants, males, Asians, less educated people, and those born outside the United States and Canada. CONCLUSIONS: Acute HEV infection is a rare cause of serum ALT flares among persons with chronic HBV. The high seroprevalence of anti-HEV IgG among the chronic HBV patients is strongly associated with various demographic factors in this largely Asian American cohort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".