Matched pair analysis of the effect of longer hypothermic machine perfusion time on kidney transplant outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it