Association between Antiviral Prophylaxis and Cytomegalovirus and Epstein–Barr Virus DNAemia in Pediatric Recipients of Allogeneic Hematopoietic Stem Cell Transplant
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV) and cytomegalovirus (CMV) infections can have serious consequences during the period of aplasia and lymphopenia following hematopoietic stem cell transplantation (HSCT). Large pediatric cohort studies examining the effect of antiviral prophylaxis against these viruses are scarce. The present study aimed to analyse the potential effect of antiviral prophylaxis (acyclovir and famciclovir) on active post-transplant EBV and CMV infection in a pediatric cohort of allogeneic HSCT recipients. METHODS: We used data from the TREASuRE cohort, consisting of 156 patients who had a first allogeneic HSCT, enrolled in four pediatric centers in Canada between July 2013 and March 2017. Follow-up was performed from the time of transplant up to 100 days post-transplant. Adjusted hazard ratio (HR) with 95% confidence intervals (CI) for the association between antiviral prophylaxis with acyclovir and/or famciclovir and EBV and CMV DNAemia was estimated using multivariate Cox regression models. RESULTS: The post-transplant cumulative incidence of EBV and CMV DNAemia at 100 days of follow-up were, respectively, 34.5% (95% CI: 27.6-42.6) and 19.9% (95% CI: 14.5-27.1). For acyclovir, the adjusted hazard ratio (HR) for CMV and EBV DNAemia was 0.55 (95% CI: 0.24-1.26) and 1.41 (95% CI: 0.63-3.14), respectively. For famciclovir, the adjusted HR were 0.82 (95% CI: 0.30-2.29) and 0.79 (95% CI: 0.36-1.72) for CMV and EBV DNAemia, respectively. CONCLUSION: The antivirals famciclovir and acyclovir did not reduce the risk of post-transplant CMV and EBV DNAemia among HSCT recipients in our pediatric population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".