Cold Machine Perfusion Versus Static Cold Storage of Kidneys Donated After Cardiac Death: A UK Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
One third of deceased donor kidneys for transplantation in the UK are donated following cardiac death (DCD). Such kidneys have a high rate of delayed graft function (DGF) following transplantation. We conducted a multicenter, randomized controlled trial to determine whether kidney preservation using cold, pulsatile machine perfusion (MP) was superior to simple cold storage (CS) for DCD kidneys. One kidney from each DCD donor was randomly allocated to CS, the other to MP. A sequential trial design was used with the primary endpoint being DGF, defined as the necessity for dialysis within the first 7 days following transplant. The trial was stopped when data were available for 45 pairs of kidneys. There was no difference in the incidence of DGF between kidneys assigned to MP or CS (58% vs. 56%, respectively), in the context of an asystolic period of 15 min and median cold ischemic times of 13.9 h for MP and 14.3 h for CS kidneys. Renal function at 3 and 12 months was similar between groups, as was graft and patient survival. For kidneys from controlled DCD donors (with mean cold ischemic times around 14 h), MP offers no advantage over CS, which is cheaper and more straightforward.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".