A new direction for cytomegalovirus prophylaxis among transplant recipients: Benefits and nonviral outcomes of letermovir use as primary CMV prophylaxis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Letermovir has changed the game of primary prophylaxis against cytomegalovirus (CMV) for hematopoietic stem cell transplant (HSCT) and more recently, solid organ transplant recipients. This is largely due to letermovir's similar efficacy in protecting against CMV reactivation and disease, along with its superior safety profile, notably reduced myelotoxicity, and lack of renal dose adjustment compared to standard agents like valganciclovir. This review will describe the potential benefits and clinical considerations of letermovir as prophylaxis among transplant recipients, with a focus on recent evidence describing nonviral outcomes of CMV. RECENT FINDINGS: Recent evidence has demonstrated improved safety (e.g., less myelosuppression) and tolerability with no difference in rates of CMV infection or disease in kidney transplant recipients given letermovir compared to valganciclovir. Real-world studies and meta-analyses in HSCT populations have explored various nonviral outcomes with letermovir use. Letermovir prophylaxis was associated with reduced mortality, lower rates of graft versus host disease, delayed CMV immune reconstitution, improved tolerability with extended durations, and decreased healthcare utilization. SUMMARY: Letermovir is an effective antiviral agent for CMV prevention and has demonstrated enhanced safety, which may allow for extended durations of primary prophylaxis among transplant recipients along with other improved clinical outcomes by mitigating the indirect effects of CMV.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".