Reduced survival and quality of life following return to dialysis after transplant failure: the Dialysis Outcomes and Practice Patterns Study
Bibliographic record
Abstract
BACKGROUND: Although dialysis after kidney transplant failure (TF) is common, the outcomes of these patients remain unclear. We compared outcomes of TF patients with transplant-naïve (TN) patients wait-listed for kidney transplantation. METHODS: We used data from the Dialysis Outcomes and Practice Patterns Study (DOPPS), including laboratory markers and health-related quality of life (HR-QOL). Mortality and hospitalization of participants with one prior TF versus TN patients were compared using the Cox regression analysis. HR-QOL physical and mental component summary scores (PCS and MCS) were examined using linear mixed models, and clinical practices were compared using logistic regression. RESULTS: Compared with TN patients (n = 2806), TF patients (n = 1856) were younger (48 versus 51 years, P = 0.003), less likely to be diabetic (18 versus 27%, P < 0.0001) and to use a permanent surgical vascular access {adjusted odds ratio (AOR): 0.85 [95% confidence interval (CI): 0.70-1.03], P = 0.10}, particularly within the first 3 months after TF [AOR 0.45 (0.32-0.62), P < 0.0001]. TF patients also had lower PCS [mean difference -2.56 (-3.36, -1.75), P < 0.0001] but not MCS [-0.42 (-1.34, 0.50), P = 0.37]. All-cause mortality [adjusted hazard ratio (AHR): 1.32 (95% CI: 1.05-1.66), P = 0.02], especially infection-related [AHR 2.45 (95% CI: 1.36-4.41), P = 0.01], was higher among TF patients. CONCLUSIONS: TF patients have reduced QOL and higher mortality, particularly due to infections, than TN patients. Interventions to optimize care before and after starting dialysis remain to be identified and applied in clinical practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".