Outcomes post kidney transplantation amongst First Nations Australians in the Northern Territory
Bibliographic record
Abstract
Aims 1)To compare graft and patient survival rates following deceased donor kidney transplantation in Northern Territory (NT) First Nations Australians between 2001–2011 and 2012-2021. 2)To compare transplant outcomes between First Nations and non-Indigenous Australians during 2012-2021. 3)To assess the impact of eplet mismatches and predicted indirectly recognizable HLA epitopes II (PIRCHE) scores on transplant outcomes in First Nations Australians. Background Despite advancements in transplant outcomes across Australia, uncertainty exists regarding improvements in graft and patient survival rates for NT First Nations Australians. No study has evaluated the impact of molecular matching on post-transplant outcomes for NT First Nations Australians. Methods We performed a retrospective cohort study involving NT First Nations Australians transplanted between 2001-2021. Participants were divided into two groups: 2001–2011 and 2012-2021. For comparison, we also included non-Indigenous recipients transplanted during the 2012–2021 period. We analyzed graft and patient survival using Kaplan-Meier curves and assessed the association of eplets and PIRCHE scores with graft outcomes and de novo donor specific antibody (dnDSA) formation. Results Five-year graft and patient survival rates were 46% and 66% in the 2001–2011 cohort compared with 69.7% and 83.1% in the 2012–2021 cohort. For non-Indigenous recipients (2012-2021), 5-year graft and patient survival were 90.5% and 97.6%. Higher eplet mismatch loads and PIRCHE scores were not associated with graft survival, patient survival, or time to rejection among First Nations Australians. Conclusion Post-transplant outcomes for First Nations Australians have improved considerably, but they remain inferior to non-Indigenous Australians.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".