Renal resistance thresholds during hypothermic machine perfusion and transplantation outcomes - a retrospective cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 it