Host and viral factors associated with serum hepatitis B virus RNA levels among patients in need for treatment
Bibliographic record
Abstract
Hepatitis B virus (HBV) RNA in serum is a novel biomarker for intrahepatic HBV replication and treatment response. For its proper use, it is essential to identify factors influencing serum HBV RNA level. Using a rapid amplification of complimentary DNA (cDNA) ends (RACE) PCR technique (lower limit of detection [LLD], 800 copies/mL [c/mL]), serum HBV RNA levels were measured in samples of 488 untreated individuals with chronic HBV infection who were eligible to treatment according to currently used recommendations. We explored the association of serum levels of HBV RNA with patient‐ and virus‐associated factors. HBV genotype distribution was 21/10/20/46/3% for A/B/C/D/other. Mean HBV RNA serum level was 5.9 (1.6) log 10 c/mL (hepatitis B e antigen [HBeAg]‐positive chronic hepatitis B [CHB], 6.5 [1.2] log c/mL; HBeAg‐negative CHB, 4.1 [1.2] log c/mL; P < 0.001). By multivariable linear regression, factors associated with lower HBV RNA level were HBeAg negativity (β = –0.69; P < 0.001), HBV genotypes A (β = –0.13; P = 0.002), B (β = –0.07; P = 0.049), and C (β = –0.61; P < 0.001) in comparison to D, and presence of HBV basal core promoter mutation either alone (β = –0.14; P = 0.001) or in combination with precore mutation (β = –0.22; P < 0.001). Higher serum alanine aminotransferase (ALT) was associated with higher HBV RNA (β = 0.23; P < 0.001). HBV RNA correlated strongly with HBV DNA (HBeAg‐pos, r = 0.72; P < 0.001; HBeAg‐neg, r = 0.78; P < 0.001) and moderately with quantitative hepatitis B surface antigen (qHBsAg; HBeAg‐pos, r = 0.54; P < 0.001; HBeAg‐neg, r = 0.19; P = 0.04) and quantitative hepatitis B surface antigen (qHBeAg; r = 0.41; P < 0.001). Conclusion: In this multiethnic cohort of 488 untreated individuals with CHB, factors associated with serum HBV RNA level were HBeAg status, serum ALT, HBV genotype, and presence of basal core promotor mutations. For the future use of serum HBV RNA as a clinical marker, it seems mandatory to take these factors into consideration. (H epatology 2018).
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".