Safety and efficacy of a reduced frequency viral monitoring strategy for Epstein‐Barr virus, cytomegalovirus, and BK polyomavirus post‐kidney transplant: A quality assurance initiative
Bibliographic record
Abstract
BACKGROUND: There is variability in recommended viral monitoring protocols after kidney transplant. In response to increased demand for laboratory testing during the COVID-19 pandemic, the Transplant Manitoba Adult Kidney Program updated its monitoring protocols for cytomegalovirus (CMV), Epstein-Barr virus (EBV), and BK polyomavirus (BKV) to a reduced frequency. METHODS: 2020. Cases included recipients transplanted after the protocol update who developed CMV, EBV, and BKV DNAemia and were matched to controls with DNAemia transplanted prior to the protocol update. The primary outcome was the difference in maximum DNA load titers between cases and matched controls. Secondary outcomes included time to initial DNAemia detection and DNAemia clearance. Safety outcomes of tissue-invasive viral disease were described. RESULTS: There were 216 recipients transplanted preupdate and 36 recipients postupdate. There was no difference between cases and controls in maximum or first DNA load titers for EBV, CMV, or BKV. Cases experienced earlier EBV DNAemia detection (26 (IQR 8, 32) vs. 434 (IQR 96, 1184) days, p = .005). Median follow-up was significantly longer for recipients transplanted preupdate (4.3 vs. 1.3 years, p < .0001). After adjusting for follow-up time, there was no difference in DNAemia clearance or tissue-invasive viral disease. CONCLUSION: Our findings suggest that reduced frequency viral monitoring protocols may be safe and cost-effective. This quality assurance initiative should be extended to detect longer-term and tissue-invasive disease outcomes.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".