Early experience with hypothermic machine perfusion of living donor kidneys - a retrospective study
Bibliographic record
Abstract
Although hypothermic machine perfusion (HMP) has been shown to be beneficial to deceased donor kidneys, the effect of HMP on living donor kidneys (LDK) is unknown. LDK are subjected to minutes of normothermic ischemia at the time of recovery. Comparison of 16 LDK preserved by HMP with 16 LDK preserved by static cold storage (SCS). Outcomes of interest are resistive indices (RI), both while on HMP and postoperatively, and creatinine clearance (CrCl). Injury markers NGAL and LDH were seen in the perfusate of LDK in amounts similar to what is found for donation after neurological determination of death kidneys. Compared to SCS kidneys, CrCl was significantly higher in the HMP group from days 2 through 7 post-transplant [ie: day 7 (78.8 ± 5.4 vs. 54.0 ± 4.6 ml/min, P = 0.005)]. CrCl at 1 year was higher in the HMP group (81.2 ± 5.8 vs. 70.0 ± 5.3 ml/min, P = 0.03). Early post-transplant RI was significantly lower in the HMP group (0.61 ± 0.02 vs. 0.71 ± 0.02, P < 0.0001). Our data support the assertion that injury does occur during LDK procurement and suggest that some of this injury may be reversed with HMP, resulting in more favorable early RI and graft function compared to SCS kidneys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".