Systematic review of hepatocellular carcinoma mortality rates among hepatitis B virus-infected renal transplant recipients, with supplemental analyses of liver failure and all-cause mortality
Bibliographic record
Abstract
OBJECTIVES: The purpose of this review was to compare the mortality rates for hepatocellular carcinoma (HCC) among hepatitis B surface antigen (HBsAg)-seropositive renal transplant (RT) patients versus HBsAg-seropositive persons of the general population. METHODS: A comprehensive search was performed to identify cohort studies of HBsAg-seropositive RT patients with at least 4 years of follow-up. Data were analyzed as outlined below. HCC was a rare event in regions of low and intermediate seroprevalence of HBsAg. Subsequently, studies from low and intermediate seroprevalence areas were analyzed separately from those of high seroprevalence areas. RESULTS: Thirty-one retrospective studies that followed 1277 seropositive RT patients were identified for inclusion. The studies were pooled and compared to four different general population studies that included 12558 seropositive persons using Poisson methods. The mortality rate of HCC was increased in low and intermediate seroprevalence areas (RR 7.67, 95% confidence interval (CI) 3.93-15.0; RR 9.92, 95% CI 5.38-18.3). In high seroprevalence areas, the mortality rate of HCC was increased compared to one population study, but not another (RR 2.76, 95% CI 1.64-4.63; RR 1.02, 95% CI 0.61-1.69). CONCLUSIONS: Mortality due to HCC was increased in low and intermediate seroprevalence areas, but the evidence was inconclusive for high seroprevalence areas.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".