Impact of Dialysis Modality on Survival after Kidney Transplant Failure
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An increasing number of patients are returning to dialysis after allograft loss (DAGL). These patients are at a higher mortality risk compared with incident ESRD patients. Among transplant-naïve patients, those treated with peritoneal dialysis (PD) enjoy an early survival advantage compared with those treated with hemodialysis (HD), but this advantage is not sustained over time. Whether a similar time-dependent survival advantage exists for PD-treated patients after allograft loss is unclear and may impact dialysis modality selection in these patients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We identified 2110 adult patients who initiated dialysis after renal transplant failure between January 1991 and December 2005 from The Canadian Organ Replacement Register. Multivariable regression analysis was used to evaluate the impact of initial dialysis modality on early (2 years), late (after 2 years), and overall mortality using an intention-to-treat approach. RESULTS: After adjustment, there was no difference in overall survival between HD- and PD-treated patients (hazard ratio((HD:PD)), 1.05; 95% confidence interval, 0.85 to 1.31), with similar results seen for both early and late survival. Superior survival was seen in more contemporary cohorts of patients returning to DAGL. CONCLUSIONS: The use of PD compared with HD is associated with similar early and overall survival among patients initiating DAGL. Differences in both patient characteristics and predialysis management between patients returning to DAGL and transplant-naive incident dialysis patients may be responsible for the absence of an early survival advantage with the use of PD in DAGL patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".