Late Cytomegalovirus Transmission and Impact of T-Depletion in Clinical Islet Transplantation
Bibliographic record
Abstract
The epidemiology of cytomegalovirus infection (CMV) in islet transplantation (IT) is not well defined. This study defines incidence, transmission and clinical sequelae of CMV reactivation or disease in 121 patients receiving 266 islet infusions at a single institution. The donor (D)/recipient (R) serostatus was D+/R- 31.2%, D+/R+ 26.3%, D-/R+ 13.2% and D-/R- 29.3%. CMV prophylaxis with oral ganciclovir/valganciclovir was given in 68%. CMV infection occurred in 14/121 patients (11.6%); six had asymptomatic seroconversion and eight others had positive viremia (six asymptomatic and two with CMV febrile symptoms). Median peak viral loads were 1755 copies/mL (range 625-9 100 000). Risk factors for viremia included lymphocyte depletion (thymoglobulin or alemtuzumab, p < 0.001). Viremia was more common in D+/R+ versus D+/R- (p = 0.12), occurring mostly late after transplant (median 306 days). Presumed transmission from IT occurred in 8/83 of D+/R- procedures (9.6%). Of the two cases of CMV disease, one resulted from islet transmission from a CMV positive donor (D+/R-); the other was due to de novo exogenous infection (D-/R-). Therefore, CMV transmission presents rarely after IT and with low incidence compared to solid organ transplantation, but occurs late posttransplant. The use of lymphocyte depleting therapies is a primary risk factor.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".