Timing of Return to Dialysis in Patients with Failing Kidney Transplants
Bibliographic record
Abstract
In the last decade, the number of patients starting dialysis after a failed kidney transplant has increased substantially. These patients appear to be different from their transplant-naïve counterparts, and so may be the timing of dialysis therapy initiation. An increasing number of studies suggest that in transplant-naïve patients, later dialysis initiation is associated with better outcomes. Very few data are available on timing of dialysis reinitiation in failed transplant recipients, and they suggest that an earlier return to dialysis therapy tended to be associated with worse survival, especially among healthier and younger patients and women. Failed transplant patients may also have unique issues such as continuation of immunosuppression versus withdrawal or the need for remnant allograft nephrectomy with regard to dialysis reinitiation. These patients may have a different predialysis preparation work-up, worse blood pressure control, higher or lower serum phosphorus levels, lower serum bicarbonate concentration, and worse anemia management. The choice of dialysis modality may also represent an important question for these patients, even though there appears to be no difference in mortality between patients starting peritoneal versus hemodialysis. Finally, failed transplant patients returning to dialysis appear to have a higher mortality rate compared with transplant-naïve incident dialysis patients, especially in the first several months of dialysis therapy. In this review, we will summarize the available data related to the timing of dialysis initiation and outcomes in failed kidney transplant patients after returning to dialysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".