Single-drug treatments for chronic hepatitis B: summarising current information by network meta-analysis
Bibliographic record
Abstract
Sirs, As pointed out by Rijckborst et al.,1 the most difficult decision in the first-line treatment for chronic hepatitis B (HB) is between pegylated interferon and nucleoside/nucleotide analogues given as monotherapy. While this decision should be made by carefully evaluating each patient, cases in whom the treatment with nucleotide analogues is preferred pose the question of choosing the best nucleotide analogue among those presently available (i.e. lamivudine, adefovir, telbivudine, entecavir and tenofovir). A recent article2 has systematically reviewed all clinical trials conducted in this area. However, if one accepts that nucleotide analogues are reserved for patients in whom the option of IFN has been excluded,1 a more selective analysis can be worthwhile in which the comparisons are restricted to nucleotide analogues given as monotherapy. To address this point, we have carried out a network meta-analysis (NetMA3) that was aimed at generating a summary NetMa graph wherein HBe antigen (Ag)-positive patients were analysed separately from HBeAg-negative patients. In both analyses, the end-point was the rate of virological response at 1 year (defined as attainment of undetectable levels of HBV DNA). Figure 1 shows the results of this NetMA. In HBeAg-positive patients (panel A), these data confirm that current effectiveness data favour entecavir and, to a lesser extent, tedofovir and adevofir; in HBeAg-negative patients (panel B), entecavir tends to be favoured as well, but the clinical trials are still too few. Nucleos(t)ide analogues given as monotherapy in the first-line treatment for hepatitis B: network meta-analysis. The graph summarises the results of both direct and indirect comparisons for HBeAg-positive patients (panel a) and HBeAg negative patients (panel b). Each direct comparison is represented by a solid line and each indirect comparison by a dotted line. The endpoint is the proportion of patients achieving virological response at 1 year. Statistical results of event rate ratio are presented as relative risk (RR) with 95% confidence interval (CI). The values of RR (with 95% CI) for direct and indirect comparisons were calculated according to the REVMAN and the ITC software, respectively. Information on the software and details about the trial-specific event rates are presented in the Supporting Information posted on the web. Symbols: ‘+’indicates which treatment is favoured at levels of statistical significance, and vice versa for ‘−’; ‘=’ denotes comparisons showing no significant difference; ‘t’ indicates which treatment is favoured by a trend in cases of no significant difference. The overall picture of the comparisons shown in Figure 1 can be a practical aid in the complex process of drug selection for an individual patient in which many factors are involved (e.g. effectiveness from clinical trials, recommendations from panels of experts, cost, cost/effectiveness as well as factors related to the individual patient concerned). The present analysis is based on the same graphical tool that we have previously applied to treatments for hepatitis C.4 Declaration of personal interests: None. Declaration of funding interests: One of the authors (DM) was supported by a grant from the Italian Society of Hospital Pharmacists. Table S1. Trial-specific rates of virological response at 1 year in HBeAg positive patients (from Woo et al. 2010): these data were used to generate the graph shown in Figure 1 (panel a). Table S2. Trial-specific rates of virological response at 1 year in HBeAg negative patients (from Woo et al. 2010): these data were used to generate the graph shown in Figure 1 (panel b). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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 | 0.037 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".