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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".