Bibliographic record
Abstract
Introduction: Controlled oxygenated rewarming of kidney grafts on ex vivo machine perfusion with acellular perfusion solution has been reported to be feasible for reconditioning of marginal grafts.Methods: We conduced small pilot clinic trial with marginal kidney grafts form extended criteria donors.N = 6 ECD kidney grafts were included in the treatment arm.After cold-storage the kidney arteria was cannulated and all six treatment arm grafts we perfused with acellular 1:1 Steen/Ringer solution with COR protocol The perfusion was done for 2 hours and kidneys were gradually rewarmed to 35° C .N = 6 controls were included in the control arm and were directly transplanted after cold-storage.Results: Primary end-point was Creatinine clearance at post-operative day 7. Secondary end-point were defined form delay graft function, graft-survival at 3th month and post-operative complication Clavien-Dindo > 3. The patient cohort was well balanced without significant differences.Age, cold-ischemic time, warm-ischemic time, kidney donor risk index were comparable between the both groups.The creatinine clearance at POD 7 was significantly higher in the treatment arm with P < 0.05.There were no significant differences in the secondary end-points between the treatment and control arm.Conclusion: Acellular ex vivo machine perfusion with controlled oxygenated rewarming improves the functional outcome of marginal ECD kidney grafts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.658 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".