Clinical Utility of Cytomegalovirus (CMV) Serology Testing in High-risk CMV D+/R- Transplant Recipients
Bibliographic record
Abstract
Late-onset cytomegalovirus (CMV) disease is a significant problem in D+/R- solid organ transplant (SOT) patients who receive antiviral prophylaxis. We assessed the clinical utility of CMV IgG and IgM serology testing for predicting late-onset CMV disease. We evaluated 352 D+/R- transplant recipients who participated in a trial comparing 100 days of ganciclovir versus valganciclovir prophylaxis. CMV serology was assessed on day 28, 56, 100, and 6 and 12 months post-transplant. IgG seroconversion occurred in 26.9% of patients by day 100, and in 63.4% and 75.3% by 6 and 12 months, respectively. IgM seroconversion occurred in 8.3%, 41.8% and 54.9% by day 100, month 6 and month 12, respectively. Seroconversion by day 100 (end of prophylaxis) was not predictive of subsequent CMV disease (CMV disease 13.3% if seropositive vs. 17.8% if seronegative; p = NS). However, at 6 months post-transplant, IgG serostatus was predictive of subsequent CMV disease between month 6 and 12 (CMV disease 1.3% if seropositive vs. 10.0% if seronegative; p = 0.002). In D+/R- patients, CMV serology testing is for the most part not clinically useful for predicting subsequent disease. However, seroconversion by 6 months may be useful for identifying patients at risk of late-onset CMV disease. Late-onset cytomegalovirus (CMV) disease is a significant problem in D+/R- solid organ transplant (SOT) patients who receive antiviral prophylaxis. We assessed the clinical utility of CMV IgG and IgM serology testing for predicting late-onset CMV disease. We evaluated 352 D+/R- transplant recipients who participated in a trial comparing 100 days of ganciclovir versus valganciclovir prophylaxis. CMV serology was assessed on day 28, 56, 100, and 6 and 12 months post-transplant. IgG seroconversion occurred in 26.9% of patients by day 100, and in 63.4% and 75.3% by 6 and 12 months, respectively. IgM seroconversion occurred in 8.3%, 41.8% and 54.9% by day 100, month 6 and month 12, respectively. Seroconversion by day 100 (end of prophylaxis) was not predictive of subsequent CMV disease (CMV disease 13.3% if seropositive vs. 17.8% if seronegative; p = NS). However, at 6 months post-transplant, IgG serostatus was predictive of subsequent CMV disease between month 6 and 12 (CMV disease 1.3% if seropositive vs. 10.0% if seronegative; p = 0.002). In D+/R- patients, CMV serology testing is for the most part not clinically useful for predicting subsequent disease. However, seroconversion by 6 months may be useful for identifying patients at risk of late-onset CMV disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".