Risk factors for late‐onset cytomegalovirus disease in donor seropositive/recipient seronegative kidney transplant recipients who receive antiviral prophylaxis
Bibliographic record
Abstract
BACKGROUND: Cytomegalovirus (CMV) disease occurs frequently after cessation of antiviral prophylaxis in CMV-seronegative kidney transplant recipients from seropositive donors (D+R-), and the risk factors are incompletely defined. METHOD: We retrospectively assessed the incidence, clinical features, and risk factors for CMV disease in a cohort of D+R- kidney transplant recipients who received antiviral prophylaxis at a single US transplant center using descriptive statistics and Cox proportional hazards models. RESULTS: CMV disease developed in 29 of 113 (26%) D+R- patients at a median of 185 days (interquartile range 116-231 days) post transplant, including CMV syndrome (66%) and tissue invasive disease (34%). The incidence of CMV disease was higher in patients who underwent re-transplantation (57% vs. 24%) and this factor was independently associated with a higher risk of CMV disease in multivariable analysis (hazard ratio, 4.02; 95% confidence interval, 1.3-13; P = 0.016). Other demographic and transplant variables were not independently associated with a risk of late-onset CMV disease. CONCLUSIONS: Despite a comprehensive analysis of patient and transplant variables, only re-transplantation was identified as a risk factor for CMV disease in D+R- kidney transplant recipients who received antiviral prophylaxis, but had limited clinical predictive value. The development of novel laboratory markers to identify patients at greatest risk for CMV disease should be a priority for future studies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".