The use of marginal kidneys in dual kidney transplantation to expand kidney graft utilization
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to chronicle the history of dual kidney transplantation (DKT) and identify opportunities to improve utilization of marginal deceased donor (MDD) kidneys through DKT. RECENT FINDINGS: The practice of DKT from adult MDDs dates back to the mid-1990s, at which time the primary indication was projected insufficient nephron mass from older donors. Multiple subsequent studies of short- and long-term success have been reported focusing on three major aspects: Identifying appropriate selection criteria/scoring systems based on pre- and postdonation factors; refining technical aspects; and analyzing longer-term outcomes. The number of adult DKTs performed in the United States has declined in the past decade and only about 60 are performed annually. For adult deceased donor kidneys meeting double allocation criteria, >60% are ultimately not transplanted. MDDs with limited renal functional capacity represent a large proportion of potential kidneys doomed to either discard or nonrecovery. SUMMARY: DKT may reduce organ discard and optimize the use of kidneys from MDDs. New and innovative technologies targeting ex vivo organ assessment, repair, and regeneration may have a major impact on the decision whether or not to use recovered kidneys for single or DKT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".