Cytomegalovirus in solid organ transplant recipients—Guidelines of the American Society of Transplantation Infectious Diseases Community of Practice
Bibliographic record
Abstract
Cytomegalovirus (CMV) is one of the most common opportunistic infections that affect the outcome of solid organ transplantation. This updated guideline from the American Society of Transplantation Infectious Diseases Community of Practice provides evidence-based and expert recommendations for screening, diagnosis, prevention, and treatment of CMV in solid organ transplant recipients. CMV serology to detect immunoglobulin G remains as the standard method for pretransplant screening of donors and transplant candidates. Antiviral prophylaxis and preemptive therapy are the mainstays of CMV prevention. The lack of a widely applicable viral load threshold for diagnosis and preemptive therapy is highlighted, as a result of variability of CMV nucleic acid testing, even in the contemporary era when calibrators are standardized. Valganciclovir and intravenous ganciclovir remain as drugs of choice for CMV management. Strategies for managing drug-resistant CMV infection are presented. There is an increasing use of CMV-specific cell-mediated immune assays to stratify the risk of CMV infection after solid organ transplantation, but their role in optimizing CMV prevention and treatment efforts has yet to be demonstrated. Specific issues related to pediatric transplant recipients are discussed.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".