Prolonged warm ischemia time is associated with graft failure and mortality after kidney transplantation
Bibliographic record
Abstract
Warm ischemia time is a potentially modifiable insult to transplanted kidneys, but little is known about its effect on long-term outcomes. Here we conducted a study of United States kidney transplant recipients (years 2000-2013) to determine the association between warm ischemia time (the time from organ removal from cold storage to reperfusion with warm blood) and death/graft failure. Times under 10 minutes were potentially attributed to coding error. Therefore, the 10-to-under-20-minute interval was chosen as the reference group. The primary outcome was mortality and graft failure (return to chronic dialysis or preemptive retransplantation) adjusted for recipient, donor, immunologic, and surgical factors. The study included 131,677 patients with 35,901 events. Relative to the reference patients, times of 10 to under 20, 20 to under 30, 30 to under 40, 40 to under 50, 50 to under 60, and 60 and more minutes were associated with hazard ratios of 1.07 (95% confidence interval, 0.99-1.15), 1.13 (1.06-1.22), 1.17 (1.09-1.26), 1.20 (1.12-1.30), and 1.23 (1.15-1.33) for the composite event, respectively. Association between prolonged warm ischemia time and death/graft failure persisted after stratification by donor type (living vs. deceased donor) and delayed graft function status. Thus, warm ischemia time is associated with adverse long-term patient and graft survival after kidney transplantation. Identifying strategies to reduce warm ischemia time is an important consideration for future study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".