Optimizing the Use of Deceased Donor Kidneys at Risk of Discard: A Clinical Practice Guideline
Bibliographic record
Abstract
Underutilization of deceased donor organs has worsened the gap in the number of kidneys available for transplantation. The purpose of this clinical practice guideline is to provide recommendations on the utilization of donor kidneys at risk of discard. Six conditional recommendations were made all with very low certainty of evidence: 1) We suggest utilizing extended criteria donor (ECD) kidneys for transplantation rather than remaining on the wait list and continuing with dialysis; 2) We suggest utilizing kidneys from ECD versus non-ECD in selected transplant candidates; 3) We suggest that organs from older kidney donors can be used in selected transplant candidates who may derive benefit from them; 4) We suggest that kidneys from deceased donors with acute kidney injury can be used for transplantation based on clinician assessment and donor factors; 5) We suggest that donor kidneys with acute kidney injury from either ECD or non-ECD be used for kidney transplantation; 6) We suggest using kidneys from donors after death determination by circulatory criteria for transplantation. This clinical practice guideline provides evidence for the use of deceased donor kidneys that are at risk of discard and may improve the shared decision-making between transplant physicians and wait-listed patients.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".