Infection Risks With Thymoglobulin Use for Delayed Graft Function in Deceased Donor Kidney Transplantation: Research Letter
Bibliographic record
Abstract
Anti-thymocyte globulin (ATG) is often used when delayed graft function (DGF) occurs post-transplantation. The ATG may be associated with an increased risk of infections but may also decrease rejection risk in high-immunological risk recipients. The safety of ATG for the indication of DGF in low-immunological risk recipients has not been well characterized. We conducted a retrospective cohort study of deceased donor kidney transplant recipients deemed low-immunological risk and not planned for ATG induction, from June 2019 to June 2023 (N = 139). Participants switched to ATG post-transplant due to DGF (exposure; N = 68) were compared to those who did not receive ATG for induction (controls; N = 71 basiliximab only induction). Outcomes examined included BK, cytomegalovirus (CMV), and serious infection as well as acute rejection, graft loss, and death. Participants who received ATG for DGF, compared to controls, were older (63.9 vs 59.7 years), more often had diabetes as cause of kidney failure (45.5% vs 33.8%) were more often recipients of death determination by circulatory criteria donor (70.5% vs 30.9%) and extended criteria donor kidneys (48.5% vs 32.3%). There was no significant difference in the probability of BK (22.1% vs 21.1%, P = .89), CMV (20.6% vs 9.9%, P = .08), serious infections (44.1% vs 43.6%, P = .96), acute rejection, graft loss, or death. The use of ATG for DGF following kidney transplantation did not significantly increase infection risk nor did it improve graft outcomes. Further studies are needed to clarify the risk-benefit trade-off of using ATG for DGF.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".