Does prolonged cold ischemia affect outcomes in donation after cardiac death donor kidney transplants?
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to analyze the combined effect of cold ischemia time (CIT) and donation after cardiac death (DCD, with requisite warm ischemia time, WIT) on kidney transplant (KT) outcomes. METHODS: Single center retrospective review of DCD KT recipients stratified by CIT. RESULTS: From 6/08 to 10/20, we performed 446 DCD KTs (115 CIT ≤20, 205 CIT 20-30, 88 CIT 30-40, 38 CIT ≥40 h). Mean WITs (26/25/27/23 min) and KDPI values (59%/55%/55%/59%) were similar while mean CITs (16.4/23.6/33.4/42.5 h) and pump times (10.3/13.6/16.1/20.4 h) differed across groups (P < .05). With a mean 6-year follow-up, patient survival (84%/84%/74%/84%) was similar. Kidney graft survival (GS) (72%/72%/56%/58%) and death censored GS (DCGS) (82%/80%/63%/67%) rates decreased whereas rates of primary nonfunction (PNF, .9%/2.4%/9.1%/7.9%) and delayed graft function (DGF) (36%/48%/50%/69%) increased with longer CIT (≥30 h, P < .05). Meaningful years free of dialysis, which we refer to as Allograft Life Years, were achieved in all cohorts (4.5/4.3/3.9/4.3 years per patient transplanted). CONCLUSION: DCD donor kidneys with prolonged CIT (≥30 h) are associated with increased rates of DGF and PNF, along with decreased GS and DCGS. Despite this, Allograft Life Years were gained even with longer CITs, demonstrating the utility of using 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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".