The effects of nucleoside analogue prophylactic treatment on <scp>HBV</scp> activation in <scp>HB</scp>c<scp>A</scp>b+ patients undergoing immunosuppressive therapy
Bibliographic record
Abstract
We investigated the effects of prophylactic nucleoside analogue treatment on HBV activation in patients with antibodies against core antigen (HBcAb+) patients undergoing immunosuppressive therapy. Patients (113), who were HBcAb+, with various autoimmune diseases, undergoing immunosuppressive therapy, were divided into two groups. The control group, not treated with antivirals, and the prophylactic group, treated with antiviral drugs. The two groups were evaluated for changes in serum biochemical marker (alanine aminotransferase ALT), virological marker (HBV DNA) and for seroconversion. In the control group, the number of patients with an increase in ALT in patients with isolated HBcAb and HBcAb and antibodies against HBsAg (HBsAb +) were five (20.0%) and one (2.8%), respectively (P < 0.05). There were six cases (24.0%) with an increase in HBV DNA in the isolated HBcAb+ subgroup and one case (2.8%) in HBsAb+/HBcAb+ subgroup (P < 0.05). In the HBcAb+ only population, six patients (24.0%) in the control group had an increase in HBV DNA compared with none in the antiviral prophylactic group (P < 0.05). One patient (4.0%) with HBcAb+ in the control group underwent an HBsAg seroconversion when receiving immunosuppressive therapy for 18 months, while none in the antiviral prophylactic group underwent reversion to HBsAg positivity (P = 0.4949). Under immunosuppressive condition, the risk of HBV activation was much higher in patients with HBcAb than in patients with both HBcAb and antibodies to HBsAb group. Antiviral prophylactic therapy could significantly reduce the risk of HBV reactivation.
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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 | 0.001 |
| 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".