Impact of Lamivudine on the Risk of Liver-Related Death in 2,041 Hbsag- and HIV-Positive Individuals: Results from An Inter-Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: The impact of lamivudine (3TC) as part of combination antiretroviral therapy (cART) on the risk of liver-related death (LRD) in HIV/hepatitis B virus (HBV)-coinfected patients has not been extensively studied. METHODS: We performed an analysis involving HIV/HBV-coinfected patients in 13 cohorts who initiated cART. The end-point was LRD--that is, death with concomitant decompensated liver disease (DLD) or hepatocellular carcinoma--as the main cause. Incidence rates of LRD after initiation of cART were expressed as number of events per 100 person-years of follow-up (PYFU). A Poisson regression model adjusted for cohort, gender, mode of HIV transmission, CD4+ T-cell count at cART initiation, liver disease pre-cART, duration of 3TC before cART, and hepatitis C virus was used to assess the association between use of 3TC and risk of LRD. RESULTS: We analysed 2,041 patients. Follow-up after starting cART was 7,648 PYFU (5,569 spent on 3TC-containing regimens) with a median per person of 48 months (range: 2-91). Of the total, 217 subjects died; 57 deaths were liver-related resulting in a rate of 7.5 per 1,000 PYFU [95% confidence intervals (CI): 5.6-9.7]. The relative risk of LRD per extra year of 3TC use was 0.73 (95% CI: 0.59-0.90, P = 0.004). CONCLUSION: The use of 3TC was associated with a reduced risk of LRD over 4 years of follow-up. This study supports the current view that the use of 3TC as part of cART should be considered in patients who are tested positive for HBsAg.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".