Serum Beta2-Microglobulin Levels in Hepatitis B e Antigen-Negative Chronic Hepatitits B Patients under Long-Term Lamivudine Monotherapy: Relationship with Virological Breakthrough
Bibliographic record
Abstract
OBJECTIVES: To evaluate the predictive value of serum beta2-microglobulin (beta2m) levels for virological breakthrough in hepatitis B e antigen-negative chronic hepatitis B patients under long term lamivudine monotherapy. METHODS: Serum beta2m levels were calculated at baseline and every three months during lamivudine monotherapy in 25 patients with chronic hepatitis B, using microparticle enzyme immunoassay technology to investigate their association with biochemical, virological and histological outcome data. Cox proportional hazard models were used to investigate the association between serum beta2m levels and virological breakthrough. RESULTS: Seven of 25 (28%), nine of 25 (36%) and 14 of 25 (56%) chronic hepatitis B patients exhibited virological breakthrough at months 12, 24 and 36 of treatment, respectively. All chronic hepatitis B patients who did not show virological breakthrough in the follow-up period exhibited beta2m elevation in month 3 of treatment. The duration (in months) of serum beta2m elevation was significantly higher in the responders group than the nonresponders group (7.3 +/- 2.6 versus 3.8 +/- 3.4, P=0.02). In contrast to patients whose serum beta2m levels were increased at three months, patients whose beta2m levels were decreased had a 4.6 times higher risk of experiencing virological breakthrough (hazards ratio 4.6, 95% CI 1.22 to 17.36). When age, pretreatment serum alanine aminotransferase and hepatitis B virus DNA levels, and grade of liver disease were simultaneously included in the same Cox model, decreased beta2m status was still associated with increased risk of virological breakthrough (hazards ratio 12.2, 95% CI 1.28 to 116.8). CONCLUSIONS: In hepatitis B e antigen-negative chronic hepatitis B patients under long term lamivudine monotherapy, serum b2m levels at three months of treatment, compared with baseline levels, are good predictors of risk for virological breakthrough.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".