An Interventional Study Using Cell-Mediated Immunity to Personalize Therapy for Cytomegalovirus Infection After Transplantation
Bibliographic record
Abstract
Cell-mediated immune responses predict clinical cytomegalovirus (CMV) events but have not been adopted into routine practice due to lack of interventional studies. Our objective was to demonstrate the safety and feasibility of early discontinuation of antivirals based on the real-time measurement of CMV-specific cell-mediated immunity (CMI) in patients with CMV viremia. Transplant patients were enrolled at the onset of CMV viremia requiring antiviral therapy. CD8 T cell responses were determined using the Quantiferon-CMV assay, and results were used to guide subsequent management. A total of 27 patients (median viral load at onset 10 900 International Units/mL) were treated until viral load negative. At end of treatment, 14/27 (51.9%) had a positive CMV-CMI response and had antivirals discontinued. The remaining 13/27 (48.1%) patients had a negative CMV-CMI response and received 2 months of secondary antiviral prophylaxis. In those with a positive CMI and early discontinuation of antivirals, only a single patient experienced a low-level asymptomatic recurrence. In contrast, recurrence was observed in 69.2% of CMI-negative patients despite more prolonged antivirals (p = 0.001). In conclusion, this is the first study to demonstrate the feasibility and safety of real-time CMV-specific CMI assessment to guide changes to the management of CMV infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".