Sex Disparity and the Uptake of Home Dialysis following Kidney Transplant Failure
Bibliographic record
Abstract
INTRODUCTION: Home dialysis modalities offer several clinical and economic benefits compared to facility-based dialysis treatment in patients with kidney failure. Studies have shown that sex and socioeconomic status (SES) disparities exist in access to dialysis and transplantation in patients with kidney failure, but whether similar disparities occur in access to home dialysis after kidney transplant failure is unknown. METHODS: Using data from the ANZDATA registry, patients who commenced dialysis after kidney transplant failure in Australia were included (2000-2020). The associations between sex and uptake of peritoneal dialysis (PD) and home hemodialysis (HHD) at 12 months after kidney transplant failure were examined using adjusted logistic regression, with interactive effect between sex and SES evaluated. RESULTS: Of 3,521 patients who experienced first kidney transplant failure, 1,352 (38%) were females. At 12 months following transplant failure, 483 (14%) were maintained on PD and 425 (12%) on HHD. Compared to females, males were less likely to select PD at 12 months after transplant failure, with an adjusted OR (95% CI) of 0.55 (0.44-0.68). The adjusted OR (95% CI) for the uptake of HHD at 12 months in males was 1.66 (1.29-2.12). There were significant interactions between sex and SES for the 12-month uptake of PD and HHD, such that for patients from socioeconomically disadvantaged areas, the respective adjusted ORs for the uptake of PD and HHD in male patients were 0.61 (0.45-0.84) and 2.25 (1.51-3.51) compared to female patients. CONCLUSION: Males who lost their kidney allografts were more likely to choose HHD over PD compared to female patients. This sex disparity was more pronounced in individuals from socioeconomically disadvantaged areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".