Return to Peritoneal Dialysis after a Hemodialysis Transfer: A Multistate Analysis of the Canadian Organ Replacement Register
Bibliographic record
Abstract
Background: Transfers to hemodialysis (HD) are challenging for patients on peritoneal dialysis (PD). Clinical outcomes after these transfers are poorly known. Methods: We analyzed patients from the Canadian Organ Replacement Registry (CORR) who received at least 30 days of PD between 2005 and 2019. All transfers from PD to facility HD were identified, regardless of their duration. Patients were followed until December 2019 for outcomes (death, kidney transplant, transfer to home HD, return to PD) and were censored if a second PD-to-HD transfer occurred. We used a multi-state modelization approach, treating different outcomes as states between which patients can sequentially transition. Results: From 18,956 patients entering PD, 9,746 (51%) patients experienced a total of 18,683 PD-to-HD transfers (mean 1.92 transfers/patient, range 1 to 27) after a median vintage of 1.8 years. Populational trajectories after these transfers are displayed in Figure 1a. A majority of patients (66%) resumed PD in the following year; this odd was highest during the first months and sharply fell afterwards (Figure 1b). Mortality rates were also high following a PD-to-HD transfer, but resuming PD remained more likely than death up to nine months after the transfer (Figure 1c). Five years after a transfer, 50% of patients had died, 6% of patients had transferred to home HD and 34% had received a transplant. Conclusion: Transfers to HD are common among patients receiving PD in Canada, and, despite a heighten post-transfer mortality risk, a majority of patients can resume PD. These findings identify the post-transfer window as a key period to enhanced clinical care (e.g. using a nurse navigator) in order to improve patients’ outcomes. Funding: Government Support – Non-U.S.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".