Use and Outcomes of Kidneys from Donation after Circulatory Death Donors in the United States
Bibliographic record
Abstract
Donation after circulatory death (DCD) donors are an important source of kidneys for transplantation, but DCD donor transplantation is less common in the United States than in other countries. In this study of national data obtained between 2008 and 2015, recovery of DCD kidneys varied substantially among the country's 58 donor service areas, and 25% of DCD kidneys were recovered in only four donor service areas. Overall, 20% of recovered DCD kidneys were discarded, varying from 3% to 33% among donor service areas. Compared with kidneys from neurologically brain dead (NBD) donors, DCD kidneys had a higher adjusted odds ratio of discard that varied from 1.25 (95% confidence interval [95% CI], 1.16 to 1.34) in kidneys with total donor warm ischemic time (WIT) of 10-26 minutes to 2.67 (95% CI, 2.34 to 3.04) in kidneys with total donor WIT >48 minutes. Among the 12,831 DCD kidneys transplanted, kidneys with WIT≤48 minutes had survival similar to that of NBD kidneys. DCD kidneys with WIT>48 minutes had a higher risk of allograft failure (hazard ratio, 1.23; 95% CI, 1.07 to 1.41), but this risk was limited to kidneys with cold ischemia time (CIT) >12 hours. We conclude that donor service area-level variation in the recovery and discard of DCD kidneys is large. Additional national data collection is needed to understand the potential to increase DCD donor transplantation in the United States. Strategies to minimize cold ischemic injury may safely allow increased use of DCD kidneys with WIT>48 minutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".