Management of cytomegalovirus in hematopoietic stem cell transplant recipients: A review of novel pharmacologic and cellular therapies
Bibliographic record
Abstract
Background: Despite diagnostic and therapeutic advances, cytomegalovirus (CMV) infection has remained a significant complication after hematopoietic stem cell transplantation (HSCT). The widespread use of pre-emptive antiviral therapy has reduced, but not eliminated, the occurrence of early CMV infection. The epidemiological shift of CMV infection, requiring repeated and prolonged treatment courses, creates an increasing need for novel antiviral drugs. This is an exciting time in the evolution of pharmacologic anti-CMV therapies. Objective: This review article provides an update on the therapeutic options for treatment of CMV in HSCT recipients, focusing on new pharmacologic agents—including maribavir, letermovir, brincidofovir, leflunomide, and artesunate—as well on as the emerging concept of cellular therapies and the future of a CMV vaccine. Results: In the past few years, encouraging preliminary data has emerged for both new pharmacologic therapies and cellular therapies; however, current evidence does not support their routine use for CMV prophylaxis or treatment. Conclusions: Despite the lack of data substantiating the routine use of new pharmacologic and cellular therapies, numerous trials, many of which are either phase III or randomized, are currently underway and will undoubtedly influence the use of these agents in the near future. CMV vaccines offer a safe and effective alternative to pharmacologic and cellular therapies as we await results of phase III clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".