Clinical Outcomes after Failed Renal Transplantation—Does Dialysis Modality Matter?
Bibliographic record
Abstract
Patients returning to dialysis after graft loss (DAGL) are an increasing segment of the end-stage renal disease (ESRD) population. It is unclear whether patients with previous graft loss have equivalent or reduced survival from the time of restarting dialysis when compared with ESRD patients initiating dialysis for the first time. Moreover, the impact of dialysis modality on the survival of patients returning to DAGL is not known. Studies of patients with transplant graft failure returning to hemodialysis (HD) have suggested decreased survival when compared with transplant-naïve dialysis patients, yet some studies of patients with graft failure returning to peritoneal dialysis (PD) have demonstrated equivalent survival. Based on these data, it is unclear whether survival differences may exist between the dialysis modalities, and if they do, whether they can be attributed to either differences in patient characteristics or to factors related to the dialysis modalities. For patients starting back onto dialysis, in whom preservation of residual renal function is important, it is also unclear how immunosuppression reduction or transplant nephrectomy may affect survival. In this review, we will summarize the available literature on survival rates of patients returning to DAGL; compare and contrast survival after initiation of HD and PD and discuss what is known about the impact of transplant nephrectomy and the different approaches to immunosuppression reduction. Practical considerations will be discussed with a specific emphasis on patients treated by PD.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".