Donor Serostatus Has an Impact on Cytomegalovirus-Specific Immunity, Cytomegaloviral Disease Incidence, and Survival in Seropositive Hematopoietic Cell Transplant Recipients
Bibliographic record
Abstract
More cytomegalovirus (CMV)-specific T cells are transferred with grafts from CMV seropositive than seronegative donors. We hypothesized that seropositive recipients of grafts from seropositive donors (D+R+) have higher counts of CMV-specific T cells than seropositive recipients of grafts from seronegative donors (D-R+), and that this is clinically relevant in the setting of in vivo T cell depletion using rabbit-antihuman thymocyte globulin (ATG). We reviewed charts of 298 ATG-conditioned, seropositive recipients for CMV reactivation (pp65 antigenemia or CMV DNAemia above institutional threshold for preemptive therapy), recurrent CMV reactivation, CMV disease, and death. In 77 of these patients, we enumerated CMV-specific T cells. Median follow-up was 564 days. CMV-specific CD4+ and, to a lesser degree, CD8+ T cell counts were higher in D+R+ than D-R+ patients. D+R+ patients had lower cumulative incidence of CMV reactivation (21% versus 48%, P < .001), recurrent reactivation (4% versus 15%, P = .003), CMV disease (3% versus 13%, P = .005) and mortality (42% versus 56%, P = .006). We conclude that in the setting of in vivo T cell depletion using ATG, seropositive donors should be used for seropositive recipients. For scenarios where only seronegative donors are available, strategies to improve CMV-specific immunity (e.g., donor vaccination) should be explored.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".