Letter: treatment of <scp>HB</scp>eAg+ chronic hepatitis B – is tenofovir truly superior to entecavir?
Bibliographic record
Abstract
Gao et al. concluded that tenofovir (TDF) was superior to entecavir (ETV) in treating HBeAg-positive chronic hepatitis B infection (CHB).1 However, the conclusions made in this study may be misleading due to important limitations in how these drugs were compared. This was not a prospective randomised head-to-head assessment of the efficacy of these two drugs, but rather a retrospective analysis of nonrandomised groups of patients. Thus, hidden biases cannot be ruled out. Making comparisons of treatment efficacy in this way may result in faulty conclusions, which lack the power to clearly guide treatment decisions. Although potentially a reflection of real-world experience, we were surprised to see such low response rates for these drugs. Randomised studies of ETV and TDF have shown undetectable HBV DNA levels after 48 weeks of therapy in HBeAg+ patients in 67%2 and 76%3 of patients, respectively, whereas Gao et al. report response rates as low 28% for ETV and 51% for TDF after 12 months of treatment. Moreover, the randomised trials of both ETV and TDF primarily assessed responses in patients with high baseline viral loads.2, 3 The conclusions are based on a very small subset of patients. Only 13 patients in the entire HBeAg+ TDF group and 6 patients in the HBeAg+ TDF group with baseline HBV DNA greater than 8 log10 IU/mL were followed up long enough to be included in the analysis after 12 months of therapy. Furthermore, the lower limit of time for follow-up in either ETV- or TDF-treated HBeAg+ patients was less than 6 months. Too few patients were followed up for too short a duration to allow useful interpretations of these results. As a result of all of this, the results of this study need to be validated in a direct randomised comparison of ETV and TDF, before they can be applied in the management of patients with CHB. Declaration of personal interests: SSL has received research funding, consultation fees and speaker's honoraria from Bristol Myers Squibb and Gilead. Declaration of funding interests: None.
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 | 0.002 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".