Factors Associated With Prolonged Warm Ischemia Time Among Deceased Donor Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Prolonged warm ischemia time (WIT) is associated with graft failure and mortality, however less is known about factors associated with prolonged WIT. METHODS: In a cohort of United States deceased donor kidney transplant recipients identified using the Scientific Registry of Transplant Recipients (Jan 2005-Dec 2013), we identified factors associated with prolonged WIT (defined as ≥ 30 minutes versus 10-30 minutes) using hierarchical multilevel models adjusting for center effect, and WIT as a continuous variable using multiple linear regression of log-transformed data. RESULTS: Among 55 829 patients, potentially modifiable risk factors associated with prolonged WIT included increased recipient body mass index (BMI) (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.44-1.72 for BMI > 35), right donor kidney (OR, 1.14; 95% CI, 1.08-1.19), and a prolonged cold ischemic time (OR, 1.23; 95% CI, 1.13-1.33 for cold ischemia time > 24 hours). Transplanting a right kidney into an obese recipient further prolonged WIT (OR, 1.75; 95% CI, 1.55-1.98; for BMI > 35), increasing overall WIT by 11.0%. There was no correlation between median WIT for a given center and annual center transplant rate (pairwise correlation coefficient, 0.0898). CONCLUSIONS: In conclusion, several modifiable factors are associated with prolonged WIT and may represent strategies to improve WIT and subsequent posttransplant outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".