Risks of hepatitis C virus reactivation in a real-life population of oncology patients treated in an academic center
Bibliographic record
Abstract
BACKGROUND: Chemotherapy has been associated with a theoretical risk of hepatitis C virus (HCV) reactivation. However, little is known about the amplitude of viral replication and the incidence of subsequent hepatic exacerbation. METHOD: We aimed to describe the occurrence of hepatitis flare and HCV reactivation at our center. We included, over a period of 5 years, adult patients with chronic HCV receiving intravenous chemotherapy. We excluded patients with undetectable HCV RNA, hepatocellular carcinoma, liver metastases or other etiologies of hepatic disease. The primary objective was to identify hepatic flares (elevation of alanine aminotransferase 3 times above the upper limit of normal). Secondary objectives were to assess viral reactivation (HCVr, HCV-RNA ≥1 log10 IU/mL when compared to baseline value), hepatic decompensation, mortality and the impact on the chemotherapy. Descriptive statistics were used. RESULTS: A total of 11 patients with chronic HCV were identified among the 5761 oncology patients. Five patients experienced a hepatic flare with median maximal ALT value of 139 U/L (IQR 133-237). Only 2 patients met criteria for HCVr with a median RNA increase of 1.16 log IU/mL (IQR 1.1-1.2). One patient presented with both HCVr and a hepatic flare. Only one patient required chemotherapy discontinuation following hepatic flare. No hepatic decompensation or related mortality were observed. CONCLUSION: We identified a very small number of HCV cases among our population. We observed HCVr and hepatic flares, but only one consequence on cancer treatment. Nonetheless, HCV screening is encouraged among patients undergoing chemotherapy to allow close follow-up of hepatic function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".