Predictors of Transfer to Home Hemodialysis after Peritoneal Dialysis Completion
Bibliographic record
Abstract
UNLABELLED: ♦ BACKGROUND: The aim of the present study was to evaluate the predictors of transfer to home hemodialysis (HHD) after peritoneal dialysis (PD) completion. ♦ METHODS: All Australian and New Zealand patients treated with PD on day 90 after initiation of renal replacement therapy between 2000 and 2012 were included. Completion of PD was defined by death, transplantation, or hemodialysis (HD) for 180 days or more. Patients were categorized as "transferred to HHD" if they initiated HHD fewer than 180 days after PD had ended. Multivariable logistic regression was used to evaluate predictors of transfer to HHD in a restricted cohort experiencing PD technique failure; a competing-risks analysis was used in the unrestricted cohort. ♦ RESULTS: Of 10 710 incident PD patients, 3752 died, 1549 underwent transplantation, and 2915 transferred to HD, among whom 156 (5.4%) started HHD. The positive predictors of transfer to HHD in the restricted cohort were male sex [odds ratio (OR): 2.81], obesity (OR: 2.20), and PD therapy duration (OR: 1.10 per year). Negative predictors included age (OR: 0.95 per year), infectious cause of technique failure (OR: 0.48), underweight (OR: 0.50), kidney disease resulting from hypertension (OR: 0.38) or diabetes (OR: 0.32), race being Maori (OR: 0.65) or Aboriginal and Torres Strait Islander (OR: 0.30). Comparable results were obtained with a competing-risks model. ♦ CONCLUSIONS: Transfer to HHD after completion of PD is rare and predicted by patient characteristics at baseline and at the time of PD end. Transition to HHD should be considered more often in patients using PD, especially when they fulfill the identified characteristics.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".