Organ donor management and delayed graft function in kidney transplant recipients: A multicenter retrospective cohort study
Bibliographic record
Abstract
Meeting donor management goals (DMGs) has been reported to decrease the incidence of delayed graft function (DGF) after kidney transplant, but whether this relationship is independent of cold machine perfusion is unclear. We aimed to determine whether meeting DMGs is associated with a reduced incidence of DGF, independent of the use of machine perfusion. We collected data on consecutive brain-dead donors and their KT recipients (KTRs) between June 2013 and December 2016 in 5 adult transplant centers. We evaluated whether DMGs were met at donor neurologic death (DND) and later time points. We defined a priori meeting optimal DMG as achieving ≥7 DMGs. Generalized estimating equations were used to predict DGF. Among 122 donors, 34% were extended-criteria donors (ECDs). The number of DMGs met increased over time (5.6 ± 1.4 at DND and 6.1 ± 1.3 at organ procurement [P < .001]). DGF occurred in 23% of 214 KTRs, and 55% received organs placed on machine perfusion. In multivariate analysis, ECD (odds ratio [OR] 2.24, 95% confidence interval [CI] 1.13-4.45), use of machine perfusion (OR 0.45, 95% CI 0.22-0.94), and optimal DMG at DND (OR 0.39, 95% CI 0.16-0.99) were associated with DGF. Early achievement of DMGs was associated with a reduced risk of the development of DGF, independent of the use of machine perfusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".