Matched pair analysis of the effect of longer hypothermic machine perfusion time on kidney transplant outcomes
Bibliographic record
Abstract
BACKGROUND Hypothermic machine perfusion (HMP) has demonstrated benefits in terms of early kidney transplant function compared to static cold storage. While longer preservation times have shown detrimental effects, a previous paired study indicated that longer pump times (the second kidney in a pair) might lead to improved outcomes. AIM To revisit the prior paired study's somewhat unexpected results by reviewing our program's experience. METHODS A total of 61 pairs of transplant recipients who received kidneys from the same donor (2012-2021) were analyzed. Patients were divided into two groups depending on whether they were transplanted first (K1) or second (K2). Therefore, the patients in each pair had identical donor characteristics, except for time on the pump. Statistical analyses included Kaplan-Meyer analysis and paired tests, including McNemar's test, student's paired t -test, or Wilcoxon's test, as appropriate. RESULTS The two groups of recipients had similar demographics (age, body mass index, diabetes, time on dialysis, sensitization and retransplants). Cold ischemic times for K1 and K2 were 8.9 (95%CI: 7.9, 9.8) and 14.7 hours (13.7, 15.8) (P < 0.0001) , respectively. Overall, K2 had a higher rate of freedom from biopsy-proven acute rejection at 1 year (P = 0.015). Delayed graft function was less common in K2, 12/61 (20%) than in K1, 20/61 (33%) (P = 0.046). Finally, K2 showed a higher graft survival than K1 (P = 0.023). CONCLUSION Our results agree with a previous study that suggested possible advantages to longer pump times. Both studies should encourage further research into HMP's potential anti-inflammatory effect.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".