Effect of patient- and center-level characteristics on uptake of home dialysis in Australia and New Zealand: a multicenter registry analysis
Bibliographic record
Abstract
BACKGROUND: Home-based dialysis therapies, home hemodialysis (HHD) and peritoneal dialysis (PD) are underutilized in many countries and significant variation in the uptake of home dialysis exists across dialysis centers. This study aimed to evaluate the patient- and center-level characteristics associated with uptake of home dialysis. METHODS: The Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry was used to include incident dialysis patients in Australia and New Zealand from 1997 to 2017. Uptake of home dialysis was defined as any HHD or PD treatment reported to ANZDATA within 6 months of dialysis initiation. Characteristics associated with home dialysis uptake were evaluated using mixed effects logistic regression models with patient- and center-level covariates, era as a fixed effect and dialysis center as a random effect. RESULTS: Overall, 54 773 patients were included. Uptake of home-based dialysis was reported in 24 399 (45%) patients but varied between 0 and 87% across the 76 centers. Patient-level factors associated with lower uptake included male sex, ethnicity (particularly indigenous peoples), older age, presence of comorbidities, late referral to a nephrology service, remote residence and obesity. Center-level predictors of lower uptake included small center size, smaller proportion of patients with permanent access at dialysis initiation and lower weekly facility hemodialysis hours. The variation in odds of home dialysis uptake across centers increased by 3% after adjusting for the era and patient-level characteristics but decreased by 24% after adjusting for center-level characteristics. CONCLUSION: Center-specific factors are associated with the variation in uptake of home dialysis across centers in Australia and New Zealand.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".