Does Expanded Criteria Donor Status Modify the Outcomes of Kidney Transplantation From Donors After Cardiac Death?
Bibliographic record
Abstract
The outcomes of kidney transplants that simultaneously exhibit donation after cardiac death (DCD) and expanded criteria donor (ECD) characteristics have not been well studied. We examined the outcomes of DCD versus non-DCD kidney transplants as a function of ECD status and the kidney donor risk index (KDRI). A cohort study of 67 816 deceased donor kidney transplant recipients (KTR), including 562 ECD/DCD KTR, from January 1, 2000 to December 31, 2009 was conducted using the Scientific Registry of Transplant Recipients. In a multivariable Cox proportional hazards model, the modestly increased risk of total graft failure in DCD versus non-DCD KTR was not significantly modified by ECD status (hazard ratio1.07 [95% CI: 1.01, 1.15] for non-ECD vs. 1.21 [95% CI: 1.04, 1.40] for ECD, p for interaction = 0.14).Moreover, the hazard ratios did not significantly vary by KDRI quintiles (p = 0.40). Similar trends were seen for death-censored graft failure and death with graft function. In conclusion, ECD status or higher KDRI score did not appreciably increase the relative hazard of adverse graft and patient outcomes in DCD KTR. These findings suggest that the judicious use of ECD/DCD donor kidneys may be an appropriate strategy to expand the donor pool.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".