Donation After Circulatory Death (DCD) Renal Allografts: The Impact of Donor Age On Recipient Outcome in a Contemporary Canadian Cohort.
Bibliographic record
Abstract
Purpose: Donation after Circulatory Death (DCD) renal allografts continue to be an under-utilized organ source. While DCD grafts have comparable outcomes to neurological determination of death (NDD) grafts, the impact of donor age has been addressed in only a limited number of studies. The aim of our study is to compare patient outcome in recipients of DCD allografts from donors greater than 50 years of age to those 50 years of age and younger. Methods: Institutional review board approved our study. 118 kidney transplant recipients received DCD allografts at our institution between July 2006 and September 2013. Recipient outcome variables (creatinine clearance (CrCl), readmission rate, length of hospital stay (LOS), or delayed graft function (DGF) ) were compared for donors older than age 50 and 50 years of age or less. Student t-test and Pearson chi-square test were used in analysis. Results: Mean recipient age was 43.4 years (SD =14.5) and median follow-up was 20.5 months (range 1.1 to 86.6). Recipients of kidney transplants from DCD donors 51 years of age and older demonstrated lower CrCl at 1 month (50.3 mL/min vs. 72.7 mL/min, p<0.001), 3 months (62.5 mL/min vs 87.9 ml/min, p = 0.002), and 1 year (66.2 mL/min vs 87.8 mL/min, p = 0.013). The two groups did not differ with regard to delayed graft function χ2 =0.573, p = 0.706, graft loss χ2 = 0.779, p = 1.00, or hospital readmission χ2 = 0.294, p = 0.355. Hospital LOS was equivalent between the two groups (13.9 days vs. 13.8 days, p = 0.929). Recipients of older DCD kidneys (>50 years of age) tended to be older (59.2 vs. 49.3, p <0.001). Conclusions: Recipients of DCD kidneys had similar short-term outcomes (DGF, LOS) between the two age categories. Recipients of the allografts from donors greater than 50 years of age demonstrated lower creatinine clearance at one year. Longer follow-up is required to determine long-term survival of these allografts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".