Can We Predict Kidney Graft Function and Graft Survival Using Hypothermic Machine Perfusion Parameters From Donors After Circulatory Death?
Bibliographic record
Abstract
Background: Hypothermic machine perfusion (HMP) reduces renal injury in donation after circulatory death donors with a high Kidney Donor Profile Index (KDPI). This study aims to characterize the correlation between KDPI, HMP parameters, and donor vitals during the withdrawal period in predicting short- and long-term graft outcomes. Methods: ANOVA with Tukey's honestly significant difference tests compared the relationship between average flow, average resistance, peak resistance, flow slope, and resistance slope on day 30, 1-y, and 3-y eGFR, and days of delayed graft function. Graft and recipient survival rates were assessed using Kaplan-Meier analysis. Results: The data for 72 grafts were suitable for analysis. Kidneys with KDPI >50% had a significantly higher day 30, and 1-y posttransplant eGFR, if HMP average flow was >150 mL/min, or the average resistance was <0.15 mm Hg/mL/min, compared with kidneys with also KDPI >50% but had not achieved the same pump parameters. There were no significant differences in the Kaplan-Meier analysis, considering recipient or graft survival, regardless of the KPDI score with 3- or 5-y outcomes. Conclusions: Use of average resistance and average flow from a HMP, in conjunction with KDPI, may be predictive of the short- and long-term function of donation after circulatory death kidney transplants.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".