Long-term impact of CMV infection on allografts and on patient survival in renal transplant patients with protocol biopsies
Bibliographic record
Abstract
Cytomegalovirus (CMV) infection is a frequent complication of early posttransplantation. This study examines its impact on chronic allograft changes, long-term graft loss, and patient survival. We studied 594 patients who had protocol biopsies at 6 wk, and 3 and 6 mo posttransplantation. Chronic allograft changes were evaluated according to the updated Banff classification [interstitial fibrosis/tubular atrophy (IF/TA), vascular and glomerular lesions]. Follow-up data were available for up to 10 yr. CMV infection was diagnosed in 153 of 594 patients (26%) in the first year after transplantation, mostly within the first 3 mo. Graft survival was reduced in patients with CMV (P = 0.03) as well as the combined allograft/patient survival (P = 0.008). Prevalence of IF/TA at 6 wk after transplantation was already threefold higher in patients who experienced CMV infection later on compared with patients without CMV (P = 0.005). In multivariate analyses, CMV viremia or disease was not a significant factor for graft loss or death. In conclusion, patients with CMV infection posttransplantation show more chronic allograft changes early on, even before CMV infection, and development of IF/TA is not more prevalent in patients with CMV. Our data do not support a significant role of CMV in patient and graft outcomes.
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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 | 0.003 |
| 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 | 0.001 | 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".