Late‐onset allograft rejection, cytomegalovirus infection, and renal allograft loss: Is anti‐CMV prophylaxis required following late‐onset allograft rejection?
Bibliographic record
Abstract
Renal transplant recipients remain at risk of delayed-onset cytomegalovirus (CMV) infection occurring beyond a complete course of prophylaxis. In this retrospective cohort, all 278 patients who received renal allografts from deceased donors from 2014 to 2016 were followed until September 1, 2019. We determined the effect of early-vs late-onset acute rejection (EAR vs LAR [ie, occurring beyond 12 months after transplantation]) on CMV infection and subsequently long-term allograft outcome. Median (IQR) duration of follow-up was 1186.0 (904.7-1531.2) days. Seventy patients including 49 patients with EAR and 21 with LAR received augmented immunosuppression. In the same interval, 40 patients developed CMV infection (36 patients beyond 90 days after transplantation [90%]). In logistic regression analysis, D+/R- CMV serostatus (OR: 5.5, 95% CI: 2.5-12.2) and LAR (OR: 7.9, 95% CI: 2.8-22.2) significantly increased the risk of CMV infection. In Cox proportional hazard model, delayed-onset CMV infection (HR: 2.51, 95% CI: 1.08-5.86) and LAR (HR: 5.46, 95% CI: 2.26-13.14) significantly increased the risk of allograft loss. Patients with LAR are at risk of late-onset CMV infection. Post-LAR, targeted prophylaxis may reduce the risk of CMV infection and subsequently allograft loss. Further studies are required to demonstrate the effect of targeted prophylaxis following LAR.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".