Epstein–Barr Virus Monitoring after an Allogeneic Hematopoietic Stem Cell Transplant: Review of the Recent Data and Current Practices in Canada
Bibliographic record
Abstract
Epstein-Barr virus-related post-transplantation lymphoproliferative disorder (EBV-PTLD) is a serious complication following hematopoietic stem cell transplantation (HSCT). A pre-emptive strategy using rituximab, which aims to manage patients early at the time of EBV reactivation to avoid PTLD, has been recommended by the most recent ECIL-6 guidelines in 2016. However, there is still a great heterogeneity of viral-load monitoring protocols, targeted patient populations, and pre-emptive treatment characteristics between centers, making precise EBV monitoring recommendations difficult. We conducted a literature review from the most recent publications between 1 January 2015 and 1 August 2023, to summarize the emerging data on EBV-PTLD prevention strategies in HSCT recipients, including the EBV-DNA threshold and use of rituximab. We also present the results of a survey of current practices carried out in 12 of the main HSCT centers across Canada. We confirm that pre-emptive rituximab remains an efficient strategy for EBV-PTLD prevention. However, there is an urgent need to perform prospective, randomized, multicentric trials with larger numbers of patients reflecting current practices to determine the best clinical conduct with regards to rituximab dosing, timing of treatment, and criteria to initiate treatments. Longer follow-ups will also be necessary to assess patients' long-term outcomes.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".