Renal resistance thresholds during hypothermic machine perfusion and transplantation outcomes - a retrospective cohort study
Bibliographic record
Abstract
Renal resistance (RR), of allografts undergoing hypothermic machine perfusion (HMP), is considered a measure of organ quality. We conducted a retrospective cohort study of adult deceased donor kidney transplant (KT) recipients whose grafts underwent HMP. Our aim was to evaluate whether RR is predictive of death-censored graft failure (DCGF). Of 274 KT eligible for analysis, 59% were from expanded criteria donor. RR was modeled as a categorical variable, using a previously identified terminal threshold of 0.4, and 0.2 mmHg/ml/min (median in our cohort). Hazard ratios (HR) of DCGF were 3.23 [95% confidence interval (CI): 1.12-9.34, P = 0.03] and 2.67 [95% CI: 1.14-6.31, P = 0.02] in univariable models, and 2.67 [95% CI: 0.91-7.86, P = 0.07] and 2.42 [95% CI: 1.02-5.72, P = 0.04] in multivariable models, when RR threshold was 0.4 and 0.2, respectively. Increasing risk of DCGF was observed when RR over the course of HMP was modeled using mixed linear regression models: HR of 1.31 [95% CI: 1.07-1.59, P < 0.01] and 1.25 [95% CI: 1.00-1.55, P = 0.05], in univariable and multivariable models, respectively. This suggests that RR during HMP is a predictor of long-term KT outcomes. Prospective studies are needed to assess the survival benefit of patients receiving KT with higher RR in comparison with staying wait-listed.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".