Peritoneal dialysis catheter removal at the time of renal transplantation: Choosing the optimal candidate
Bibliographic record
Abstract
INTRODUCTION: Concurrent peritoneal dialysis (PD) catheter removal during renal transplantation is controversial, with limited evidence supporting this practice. Our objective was to determine the rate of delayed graft function (DGF) in patients on preoperative PD. Additionally, we sought to identify which patients can safely have their PD catheter removed during transplantation due to a low risk of DGF. METHODS: We conducted a retrospective observational study between June 2011 and December 2015. The primary outcome was the diagnosis of DGF, defined as the need for dialysis within the first week of transplantation. Clinical and transplant factors, including graft type and donor criteria, were assessed for association with the primary outcome. Catheter-related complication rates were also compared between post-transplant PD and hemodialysis (HD). RESULTS: Of our cohort of 567 patients, 145 patients (25.6%) developed DGF. Obesity (odds ratio [OR] 1.06; 95% confidence interval [CI] 1.00-1.11; p=0.04) and increased perioperative blood loss (OR 1.002; 95% CI 1.000-1.003; p=0.03) were predictors of DGF. Protective factors included living donor (LD) grafts (OR 0.15; 95% CI 0.05-0.49; p=0.002) and intraoperative graft urine production (OR 0.39; 95% CI 0.23-0.65; p<0.001). In our PD cohort, only LD grafts demonstrated lower DGF rates (0 LD vs. 20.8% deceased donor; p=0.003). In terms of post-transplant renal replacement therapy, patients on PD and HD had similar duration of temporary dialysis (one day PD vs. two days HD; p=0.48) and catheter-related complication rates (4.5% PD vs. 2.6% HD; p=0.30). CONCLUSIONS: Carefully selected patients, such as those receiving LD grafts, may benefit from concurrent PD catheter removal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".