Normothermic ex vivo kidney perfusion for graft quality assessment prior to transplantation
Bibliographic record
Abstract
Normothermic ex vivo kidney perfusion (NEVKP) represents a novel approach for graft preservation and functional improvement in kidney transplantation. We investigated whether NEVKP also allows graft quality assessment before transplantation. Kidneys from 30-kg pigs were recovered in a model of heart-beating donation (group A) after 30 minutes (group B) or 60 minutes (group C) (n = 5/group) of warm ischemia. After 8 hours of NEVKP, contralateral kidneys were resected, grafts were autotransplanted, and the pigs were followed for 3 days. After transplantation, renal function measured based on peak serum creatinine differed significantly among groups ( P < .05). Throughout NEVKP, intrarenal resistance was lowest in group A and highest in group C ( P < .05). intrarenal resistance at the initiation of NEVKP correlated with postoperative renal function ( P < .001 at NEVKP hour 1). Markers of acid-base homeostasis (pH, HCO 3 – , base excess) differed among groups ( P < .05) and correlated with posttransplantation renal function ( P < .001 for pH at NEVKP hour 1). Similarly, lactate and aspartate aminotransferase were lowest in noninjured grafts versus donation after circulatory death kidneys ( P < .05) and correlated with posttransplantation kidney function ( P < .001 for lactate at NEVKP hour 1). In conclusion, assessment of perfusion characteristics and clinically available perfusate biomarkers during NEVKP allows the prediction of posttransplantation graft function. Thus, NEVKP might allow decision-making regarding whether grafts are suitable for transplantation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".