Letter to the Editor: Benefits of stopping therapy in patients with cirrhotic hepatitis B, true effect or residual confounding?
Bibliographic record
Abstract
We have read with great interest the article by Jeng et al1 in which they reported a decreased HCC risk and mortality rate in patients with cirrhosis who stopped nucleo(s)tide analog therapy compared with patients who continued nucleo(s)tide analog therapy. These findings are highly interesting and may provide new insights into the anticarcinogenic mechanisms in patients with chronic hepatitis B. However, these findings contradict the current paradigm, especially since nucleo(s)tide analog therapy has previously been shown to decrease the risk of progressive liver disease, decompensation, and HCC, and that even a low level of viremia is a risk factor for HCC development.2,3 Furthermore, stopping therapy in patients with cirrhosis is controversial, due to the fact that cirrhosis is a risk factor for hepatic flares and decompensation.4,5 The authors hypothesized that reactivation of the immune system, resulting in a decrease in HBsAg levels, resulted in improved immune protection against HCC by reducing the transcription of integrated HBV DNA and increasing antitumor immunity. Interestingly, [...]
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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".