A prospective multicenter observational study of cell-mediated immunity as a predictor for cytomegalovirus infection in kidney transplant recipients
Bibliographic record
Abstract
T cell immunity is essential for the control of cytomegalovirus (CMV) infection after transplantation. We evaluated a CMV-specific peptide-based enzyme-linked immunosorbent spot (ELISPOT) assay to determine whether assay results could predict subsequent CMV events. Adult kidney transplant recipients at 43 centers underwent ELISPOT testing to enumerate interferon gamma (IFN-γ) binding spot-forming units (sfu) after stimulation of cells with an overlapping peptide pool of CMV phosphoprotein 65 (pp65) and immediate early-1 (IE-1) protein at the end of antiviral prophylaxis (EOP) and various time points thereafter. The primary outcome was a CMV event in the first posttransplant year. In 583 kidney transplant recipients (260 seropositive donor [D+]/seronegative recipient [R−] and 277 R+), CMV events occurred in 44 of 368 eligible patients (11.8%) at a median of 227 days (range 92-360) posttransplant. A cutoff value of >40 sfu/2.5 × 10 5 cells for either IE-1 or pp65 was derived as a threshold for positivity, with a negative predictive value of >97% for CMV events. CMV events were significantly lower in assay positive vs assay negative patients (3.0% vs 19.5%, P < .0001 for pp65). Time to CMV event post-EOP was significantly greater in those with sfu >40 at EOP ( P < .0001). In this large, multicenter trial of kidney transplant recipients, we show that an assessment of CMV-specific immunity using a novel ELISPOT assay is able to predict protection from 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".