Virus-Specific T-Cell Therapy for Prophylaxis and Treatment of Cytomegalovirus Infections After Transplantation: a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Cytomegalovirus (CMV) infection is a leading complication following hematopoietic stem cell transplant (HSCT) and solid organ transplant (SOT). Virus-specific T cells (VSTs) have been used for the prophylaxis and treatment of CMV infections. We conducted a scoping review to catalogue and characterize the existing literature. METHODS: Systematic searches were performed in collaboration with an expert librarian. Inclusion criterion was the use of CMV-VST for prophylaxis or treatment in HSCT and SOT patients. Major exclusion criteria were case reports and series with <5 cases. Databases were queried from inception to 31 May 2024. Of the 2587 identified abstracts, full text review was performed on 92 articles, and 67 studies underwent final data extraction. RESULTS: Most studies were in the HSCT population. The CMV infection rate was 28% (interquartile range [IQR], 14%-44%) when CMV-VSTs were used as prophylaxis. Response rates for non-refractory and/or resistant (R/R) infections and R/R infections in HSCT patients were 98% (IQR, 70%-100%) and 70% (IQR, 56%-88%), respectively. Four studies included SOT patients with R/R infections, demonstrating a response rate of 15%-64%. Variables including donor/recipient serostatus and antiviral use were heterogeneously reported, and various definitions of CMV infection and response were used. CMV-VSTs were well-tolerated with minimal adverse events reported. CONCLUSIONS: CMV-VSTs are more commonly used in HSCT patients with limited data in SOT patients, and differential reporting of key variables precludes extrapolation. A standardized registry should be considered for future studies with additional focus on the optimal dosing, timing, and interaction with concurrent antivirals.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".