A Study of the Pharmacokinetic Profile of Low-Dose Hepatitis B Immune Globulin in Long-Term Liver Transplant Recipients for Chronic Hepatitis B Infection
Bibliographic record
Abstract
Post-transplant protocols for hepatitis B (HBV) prophylaxis using high-dose intravenous hepatitis B immune globulin (10,000 IU) with or without lamivudine are commonly reported. Our centre has previously reported a low-dose intramuscular (i.m.) protocol and lamivudine with excellent results. There have been, however, no pharmacokinetic studies of i.m. hepatitis B immune globulin (HBIG) in this setting. The objective of this study was to determine the pharmacokinetic profile of i.m. HBIG in long-term post-liver-transplant recipients to determine a rational dosing protocol. Six stable liver transplant recipients receiving monthly i.m. HBIG injections for greater than one year were enrolled in this study. All patients had no detectable HBV DNA levels. HBIG titers (anti-HBs) were measured predose, then three times weekly for four weeks and then twice weekly until the serum HBIG titers were 100 IU/L or less. The pharmacokinetic parameters were calculated using noncompartment methods. The mean time to maximum concentration was 10.5 d (range 4-20 d) and the mean half-life was 20 d (range 13.5-23.5 d). Based on these pharmacokinetic parameters in stable long-term post-transplant patients, a rational dosing protocol was developed that allows for more appropriate utility of HBIG and improved patient convenience.
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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 | 0.002 |
| 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 | 0.001 |
| 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".