Virologic response and resistance to adefovir in patients with chronic hepatitis B
Bibliographic record
Abstract
BACKGROUND: The incidence and risk factors for adefovir-resistant HBV have not been clearly defined. AIMS: To characterize the virologic response to adefovir, to determine the rate of adefovir resistance and to explore factors associated with initial virologic response (IVR) and adefovir resistance. METHODS: All hepatitis B patients who received adefovir for > or =6 months at our center were prospectively monitored for virologic response and adefovir resistance. RESULTS: Forty three patients were included; mean treatment duration was 18 months (range 6-45). Thirty four (79%) patients had prior lamivudine. IVR was observed in 44% patients and associated with higher pretreatment ALT (P = 0.05) and the absence of HBeAg (P = 0.02). Six (14%) patients were found to have adefovir-resistant mutations. The cumulative probability of genotypic resistance to adefovir at month 24 was 22%. Patients with adefovir resistance were more likely to have been switched from lamivudine to adefovir monotherapy (P = 0.01), to be older (P = 0.04), and to be infected with HBV genotype D (P = 0.02). CONCLUSIONS: Roughly 50% of patients failed to achieve IVR on adefovir. The cumulative probability of adefovir resistance at 2 years was 22%. Our data suggest that combination of lamivudine and adefovir may prevent emergence of adefovir resistance in patients with lamivudine-resistant HBV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".