Bibliographic record
Abstract
PURPOSE OF REVIEW: Kidney transplantation is vital for those with end-stage renal disease, enhancing quality of life and longevity. It is the preferred treatment but is hindered by a global disparity between donor kidney availability and demand. Therefore, optimizing organ storage techniques is crucial to mitigate the effects of ischemia reperfusion injury in available organs. Recent interest has centered on innovative methods like oxygenated normothermic perfusion and abdominal regional perfusion. RECENT FINDINGS: Multiple recent metanalyses, including a Cochrane review, confirm the benefits of hypothermic machine perfusion (HMP) for deceased donor kidneys, demonstrating its utility and cost effectiveness. The benefits of oxygenated normothermic perfusion have been seen in retrospective data sets but not in prospective trials. Abdominal regional perfusion (aNRP) is gaining interest, especially for liver transplantation, but kidney specific data are scant. SUMMARY: High-quality evidence backs the use of HMP for deceased donor kidneys. Despite interest in other techniques, clinical evidence for their benefits in kidney transplantation is lacking. The gap between innovation and verified success emphasizes the need for continued research and collaboration between medical professionals, researchers, and ethical committees. This review aims to further illuminate the complexities and advancements in the field, bridging the knowledge gap and aiding in the continual pursuit of excellence in 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".