Cytomegalovirus seromismatching increases the risk of acute renal allograft rejection.
Bibliographic record
Abstract
BACKGROUND: There is an association between cytomegalovirus (CMV) infection or disease and acute allograft rejection in the setting of renal transplantation. There is, however, debate regarding the nature of this association, with evidence supporting both a "forward" relationship (CMV infection or disease precedes acute rejection) and a "backward" relationship (CMV infection or disease follows acute rejection). The objective of this study was to determine whether CMV matching had an independent effect on the risk of acute renal allograft rejection, which would support the view that CMV infection or disease is a risk factor for acute rejection. METHODS: Retrospective single center study (using a prospectively maintained database) of 333 first cadaveric transplant recipients from January 1st 1991 to December 31st 1997. Primary end-point was incidence of acute rejection, diagnosed clinically or by renal biopsy, for different groups formed on the basis of CMV seromatching. RESULTS: One hundred and ninety-four patients (58.3%) had at least one acute rejection episode. CMV seromismatched patients (donor +/recipient-) had a significantly higher rate of acute rejection than non-seromismatched patients (72.6% vs. 54.2%, P=0.005). Using multiple logistic regression, CMV seromismatch, delayed graft function, and biological induction were identified as independent predictors of acute rejection. The adjusted odds ratios for these were 2.28, 1.65, and 0.52, respectively. CONCLUSIONS: Patients who are CMV seromismatched are at higher risk of acute renal allograft rejection. This finding suggests that CMV infection or disease is a risk factor for acute rejection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".