Renal histopathology features according to various warm ischemia times in porcine laparoscopic and open surgery model
Bibliographic record
Abstract
BACKGROUND: Thirty minutes has been considered as the threshold for tolerable warm ischemic time (WIT). Recent reports demonstrate recovery of renal function after longer WIT. We assessed renal histology according to different WIT in a 2-kidney porcine model. METHODS: Twelve female pigs were randomized to an open or laparoscopic group. Each pig was further randomized within each group to clamping the left renal artery for 5, 15, 30, 45, 60 or 180 minutes. Preclamping left renal biopsies were performed on each pig. The contralateral kidney in each animal was used as an individual control. On postoperative day 14, all animals underwent bilateral nephrectomies. Preclamping left renal biopsies and all renal specimens were evaluated by a blinded veterinary pathologist. RESULTS: One pig died in the open group after 180 minutes of clamping. Histopathology did not show any significant changes between the two groups and across clamp times from 5 to 60 minutes. After 180 minutes of laparoscopic clamping, there was evidence of diffuse necrosis. INTERPRETATION: Sixty minutes of ischemia did not show any permanent renal damage in both groups. Further studies are needed to verify these findings in humans. A prolonged ischemic time without permanent renal damage would be helpful in partial nephrectomy. Warm ischemic time of 180 minutes exceeded the renal ischemic burden based on histological features.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".