Peritoneal dialysis versus hemodialysis in patients with delayed graft function
Bibliographic record
Abstract
Delayed graft function (DGF) in kidney transplantation affects adverse outcomes. It remains unclear whether the post-transplant dialysis modality alters perioperative or long-term graft outcomes. We performed a retrospective observational quality initiative at two Canadian renal transplant centers, in which DGF occurred in the recipient, necessitating one of peritoneal dialysis (PD) or hemodialysis (HD). There was no difference in baseline factors between patients with post-transplant PD (n = 14) or HD (n = 63). The use of PD was associated with an increased risk of wound infection/leakage (PD 5/14 vs. HD 6/63, p = 0.024), shorter length of hospitalization (13.7 vs. 18.7 d, p = 0.009) and time requiring dialysis post-operatively (6.5 vs 11.0 d, p = 0.043). There were no differences in readmission to hospital within 6 months (4/14 vs. 23/63, p = 0.759), graft loss (0/14 vs. 2/63, p = 1.000) or acute rejection episodes (1/14 vs. 4/63, p = 1.000) at one yr, and GFR did not differ between the PD or HD groups at 30 d (35.7 vs. 33.8 mL/min/m(2), p = 0.731), six months (46.9 vs. 45.5 mL/min/m(2), p = 0.835) or one yr (46.6 vs. 44.5 mL/min/m(2), p = 0.746). Further research is needed to determine which transplant patients are most appropriate to undergo PD catheter removal at the time of transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".