P1431MULTI-CENTER REGISTRY ANALYSIS COMPARING SURVIVAL ON HOME HEMODIALYSIS AND KIDNEY TRANSPLANT RECIPIENTS IN AUSTRALIA AND NEW ZEALAND
Bibliographic record
Abstract
Abstract Background and Aims In the era of organ shortage, home hemodialysis (HHD) has been identified as the possible preferential bridge to kidney transplantation. Data are conflicting regarding the comparability of HHD and transplantation outcomes. This study aimed to compare patient and treatment survival between HHD patients and kidney transplant recipients. Method The Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry was used to include incident HHD patients on day 90 after initiation of kidney replacement therapy and first kidney-only transplant recipients in Australia and New Zealand from 1997 to 2017. Survival times were analyzed using the Kaplan-Meier product limit method comparing HHD patients to subtypes of kidney transplant recipients using the log-rank test. Adjusted analyses were performed with multivariable Cox proportional hazards regression models for time to all-cause mortality. Time-to-treatment failure or death was assessed as a composite secondary outcome. Results The study compared 1411 HHD patients to 4960 living donor (LD) recipients, 6019 standard criteria donor (SCD) recipients and 2427 expanded criteria donor (ECD) recipients. While LD and SCD recipients had reduced risks of mortality compared to HHD patients (LD adjusted hazard ratio [HR] 0.57, 95%CI 0.46-0.71; SCD HR 0.65 95%CI 0.52-0.79), the risk of mortality was comparable between ECD recipients and HHD patients (HR 0.90, 95%CI 0.73-1.12). LD, SCD and ECD kidney recipients each experienced superior time-to-treatment failure or death compared to HHD patients. Conclusion This large registry study showed that kidney transplant offers a survival benefit compared to HHD but that this advantage is not significant for ECD recipients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".