Hepatitis B Screening Practices and Associated Outcomes for Patients Receiving Rituximab Therapy in a Tertiary Care Centre
Bibliographic record
Abstract
Background. Hepatitis B reactivation is an important risk of rituximab therapy. As rituximab is used more widely by an increasing number of specialists, opportunities to prevent reactivation may be missed due to lack of awareness or protocolization. This study aimed to assess Hepatitis B screening in our rituximab patients. Methods. All adult patients receiving rituximab therapy in 2014/2015 at our center (832 beds; catchment 850,000) were included. Available serology at the time of initiation were recorded. Patients found to have active hepatitis B (DNA/surface antigen) or core antibody were evaluated for evidence of monitoring or prophylaxis and for adverse outcomes. Results. Three hundred eighteen patients received rituximab in the study period, 85% of whom received some form of hepatitis B serologic testing (table). In the absence of hepatitis B surface antigen, nearly 1 in 4 were not tested for core antibody. In those with core antibody, 32.5% did not have regular monitoring for or prophylaxis against reactivation. One (2.5%) of these patients developed a fatal fulminant hepatitis B flare. Of the patients not screened, one (2%) developed a clinical hepatitis B reactivation. The majority of transaminase elevation was unrelated to hepatitis B. Conclusion. A systematic protocol operationalizing hepatitis prevention guidelines in patients receiving rituximab is required in centers where one does not exist. *Includes HIV patients on HBV active HAART (n = 3 and 3 from top to bottom) Disclosures. All authors: No reported disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".