Association Between First Post-operative Day Urine Output Following Kidney Transplantation and Short-Term and Long-Term Outcomes: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: The relationship between post-operative urine output (UO) following kidney transplantation and long-term graft function has not been well described. Objective: In this study, we examined the association between decreased UO on post-operative day 1 (POD1) and post-transplant outcomes. Design: This is a retrospective cohort study. Setting: Atlantic Canada. Patients: Patients from the 4 Atlantic Canadian provinces (Nova Scotia, New Brunswick, Newfoundland, and Prince Edward Island) who received a live or deceased donor kidney transplant from 2006 through 2019 through the multiorgan transplant program at the Queen Elizabeth II Health Sciences Centre (QEII) hospital in Halifax, Nova Scotia. Measurements: Using multivariable Cox proportional hazards models, we assessed the association of low POD1 UO (defined as ≤1000 mL) with death-censored graft loss (DCGL). In secondary analyses, we used adjusted logistic regression or Cox models as appropriate to assess the impact of UO on delayed graft function (DGF), prolonged length of stay (greater than the median for the entire cohort), and death. Results: Of the 991 patients included, 151 (15.2%) had a UO ≤1000 mL on POD1. Low UO was independently associated with DCGL (hazard ratio [HR] = 4.00, 95% confidence interval [CI] = 95% CI = 1.55-10.32), DGF (odds ratio [OR] = 45.25, 95% CI = 23.00-89.02), and prolonged length of stay (OR = 5.06, 95% CI = 2.95-8.69), but not death (HR = 0.81, 95% CI = 0.31-2.09). Limitations: This was a single-center, retrospective, observational study and therefore has inherent limitations of generalizability, data collection, and residual confounding. Conclusions: Overall, reduced post-operative UO following kidney transplantation is associated with an increased risk of DCGL, DGF, and prolonged hospital length of stay.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".