The Utility of Pulsatile Perfusion Across the Spectrum of Deceased Donors Using the KDPI.
Bibliographic record
Abstract
Pulsatile perfusion (PP) is mostly used with high risk donor kidneys, while the utility of PP with low risk donor kidneys remains debatable. With the advent of the kidney donor profile index (KDPI) standard criteria deceased donors may be further categorized based on established risk factors. We sought to further refine the criteria for PP use, by examining the impact of PP on the risk of delayed graft function (DGF) for each decile of KDPI. Methods: All deceased donor kidney transplant recipients between 1995-2010 were identified using SRTR and stratified into deciles of KDPI based on their kidney donor risk index. Within each decile, multivariate logistic regression models were used to determine the odds of DGF in cases where PP was used versus cold storage (CS) after adjustment for the following recipient and transplant characteristics: age, gender, race, BMI, pre-transplant dialysis duration, cause of ESRD, repeat transplantation, cold ischemic time, warm ischemic time, and level of HLA-ABDR mismatch. Results: After adjustment for CIT, PP was associated with a reduced risk of DGF in all deciles of KDPI, except the lowest risk decile (adjOR 0.78 (0.56, 1.08)). When stratified by CIT categories, PP was consistently associated with a reduced odds of DGF when the KDPI was >0.30, even when the CIT was less than 12 hours. Conclusion: PP is associated with a reduced risk of DGF across a large subset of standard criteria donor kidneys (when the KDPI >0.30) irrespective of CIT, but the benefit of PP with KDPI<0.30 remains questionable.Table: No Caption available.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".