The Relative Effects of Warm and Cold Ischemic Injury in an Experimental Model of Nonheartbeating Donor Kidneys
Bibliographic record
Abstract
BACKGROUND: Ischemia reperfusion injury (I/R) leads to delayed graft function and remains an important problem in renal transplantation. The aim of this experimental study was to assess the effects of warm (WI) and cold ischemia (CI) in models of heartbeating (HBD) and controlled/uncontrolled nonheartbeating donor (NHBD) kidneys. METHODS: A reperfusion model utilizing cardiopulmonary bypass technology was used to perfuse isolated porcine kidneys with autologous blood after the following conditions: 0 min WI+2 h cold storage (CS); 0 min WI+18 h CS; 10 min WI+2 h CS; 10 min WI+18 h CS; 25 min WI+2 h CS; 25 min WI+18 h CS. Renal function was measured over a period of 3 hr. RESULTS: Renal functional parameters were not significantly different between 0, 10, 25 WI with 2 h CS [AUC creatinine (Cr) decrease of 1057+/-177, 1102+/-260, and 1245+/-143 micromol/L h, P=0.338; AUC creatinine clearance (CrCl) of 37.7+/-15.8, 36.2+/-21.7, 19.8+/-9.1 ml/min/100 g h, P=0.099]. After 18 h CS, renal function was severely impaired in the 10 and 25 WI groups compared to 0 min WI [AUC Cr of 2156+/-401, 2287+/-148, 1563+/-395 micromol/L h, P=0.037; AUC CrCl of 2.2+/-1.7, 1.5+/-1.5, 21.7+/-13.4 ml/min/100 g h, P=0.007). CONCLUSION: Warm ischemia of up to 25 min was only detrimental to renal function when kidneys were subsequently preserved in cold storage for 18 hr. This data suggests that limiting the cold storage period is of paramount importance when transplanting kidneys subjected from nonheartbeating donors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".