Cytomegalovirus surveillance after antiviral prophylaxis in CMV mismatched transplant patients: Does recurrent cytomegalovirus DNAemia impact patient survival?
Bibliographic record
Abstract
BACKGROUND: Cytomegalovirus (CMV) mismatched, donor IgG-positive/recipient IgG-negative, solid organ transplant recipients (SOTRs) are at high risk of CMV invasive disease. Post-prophylaxis disease is an issue in this population. Some programs employ surveillance after prophylaxis (SAP) to limit the incidence of post-prophylaxis disease. METHODS: This was a single-center retrospective cohort study that included all CMV mismatched SOTRs from 2003 to 2017. Patients underwent SAP with weekly CMV plasma viral load for 12 weeks. The subjects were classified into three post-prophylaxis DNAemia patterns: no DNAemia, one episode of DNAemia, and multiple episodes of DNAemia. We calculated the cumulative incidence of each DNAemia pattern. We also determined 5-year mortality based on DNAemia pattern stratified by organ transplant type. RESULTS: Post-prophylaxis recurrent DNAemia occurred in 63% of lung recipients and 32% of non-lung recipients (p = .003). Tissue invasive CMV disease was diagnosed in 3% of the population and CMV syndrome was diagnosed in 33%. Recurrent DNAemia was not associated with 5-year mortality. CONCLUSION: In this cohort, undergoing SAP tissue invasive disease was uncommon and CMV DNAemia recurrence did not have an impact on long-term mortality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".