Cytomegalovirus transmission in mismatched solid organ transplant recipients: Are factors other than anti-viral prophylaxis at play?
Bibliographic record
Abstract
Although antiviral prophylaxis has reduced cytomegalovirus (CMV) DNAemia and disease in seronegative solid organ transplant (SOT) recipients (R-) receiving seropositive donor organs (D+), its impact on CMV transmission is uncertain. Transmission, defined as CMV antigenemia/CMV DNAemia and/or seroconversion by year 2, and associated demographic risk factors were studied retrospectively in 428 D+/R- and 429 D-/R- patients receiving a SOT at our center. The cumulative transmission incidence was higher for lung (90.5%) and liver recipients (85.1%) than heart (72.7%), kidney (63.9%), and pancreas (56.2%) recipients (p < .001) and was significantly lower in living (50.1%) versus deceased donor (77.4%, p < .001) kidney recipients despite identical antiviral prophylaxis. In multivariate analysis, only allograft type predicted transmission risk (HR [CI] lung 1.609 [1.159, 2.234] and liver 1.644 [1.209, 2.234] vs kidney). For 53 D+ donating to >1 R- with adequate follow-up, 43 transmitted to all, three transmitted to none, and seven transmitted inconsistently with lungs and livers always transmitting but donor-matched heart, kidney or kidney-pancreas allografts sometimes not. Kidney pairs transmitted concordantly. CMV transmission risk is allograft-specific and unchanged despite antiviral prophylaxis. Tracking transmission and defining donor factors associated with transmission escape may provide novel opportunities for more targeted CMV prevention and improve outcome analysis in antiviral and vaccine trials.
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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 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".