Can HB vaccine yield a booster effect on individuals with positive serum anti-HBs and anti-HBc markers?
Bibliographic record
Abstract
AIM: To evaluate if HB vaccination can yield a booster effect on the anti-HBs level of those naturally acquired HBV positive markers. METHODS: Sera were collected from 1399 newly enrolled university students aged between 18-20 years at the entrance medical examination in 2001. Forty-four students (28 males and 16 females) with positive serum anti-HBs and anti-HBc markers served as an observation group and another 44 students (24 males and 20 females) without any HBV markers as the control. HB vaccination was given to all the students without positive serum HBsAg according to 0, 1, 6 month regimen and the peripheral venous blood was sampled from those of both observation and control groups for anti-HBs detection one month after the second and third doses. Anti-HBs levels were measured by ELISA. RESULTS: The seroconversion rate of anti-HBs in the control group was 100% after the second dose, but the geometric mean titers (GMTs) were low. The tendency of serum anti-HBs changes after the 3rd dose was completely different between the two groups. Although more than half of those with positive anti-HBs and anti-HBc showed a mild increase of anti-HBs levels after the 2nd boosting dose (mean anti-HBs level was 320:198 mIU), but the increase of serum anti-HBs titer was much smaller than that in the control group. The averages of their initial serum anti-HBs levels and the levels after the 2nd and 3rd doses were 198, 320 and 275 mIU respectively. All the subjects from the control group had an obvious increase in their serum anti-HBs levels which was nearly 4 times the baseline level (302:78 mIU). CONCLUSION: HB vaccination can not enhance anti-HBs levels in those with positive serum anti-HBs and anti-HBc markers.
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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 | 0.001 |
| 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".