Impact of Donation After Circulatory Death on Outcomes of Expanded Criteria Donor Kidney Transplants
Bibliographic record
Abstract
Background: Expanded criteria donor (ECD) kidneys experience suboptimal outcomes compared to standard criteria donor (SCD) kidneys. Methods: To examine the additional impact of deceased organ category, donation after circulatory death (DCD) and neurological determination of death (NDD), on ECD outcomes, we examined 1- & 3-year patient and graft survival in all ECD kidney recipients in our institution between January 2008 and December 2017. Results: Of 166 ECD recipients, 49 (29.5%) were DCD and 117 (70.5%) were NDD. Delayed graft function was higher in the DCD/ECD group 61.2 % vs 32.0 % among NDD/ECD recipients. Graft loss was significantly increased among DCD/ECD (HR for graft loss 4.81 (95% CI, 1.78, 13.01), p value 0.002 at 1 year and 2.03 (95% CI, 1.03, 4.0), p value 0.042 at 3 year). Death-censored graft loss was higher among DCD/ECD (HR was 10.12 (95% CI, 2.14, 47.92), p value 0.004 at 1 year and 2.83 (95% CI, 1.24, 6.46) p value 0.014 at 3 years). There was no statistically significant difference in all-cause mortality. Conclusions: Our study demonstrated that DCD/ECD kidneys have lower graft survival compared to NDD/ECD kidneys. Time on dialysis, waiting time and panel reactive antibody should be taken into account when offering these organs to 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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".