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Record W2751652599 · doi:10.1097/tp.0000000000001911

Organ Transplantation in Australia

2017· letter· en· W2751652599 on OpenAlexaboutno aff
Paul Lawton, Stephen McDonald, Paul Snelling, Jaquelyne T. Hughes, Alan Cass

Bibliographic record

VenueTransplantation · 2017
Typeletter
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTransplantationDialysisMedicineKidney transplantationPopulationKidney diseaseRenal replacement therapyIncidence (geometry)Intensive care medicineSurgeryInternal medicineEnvironmental healthBiology

Abstract

fetched live from OpenAlex

We agree with the authors about the successes of organ transplantation in Australia.1 However, we would like to highlight the lack of access to kidney transplantation and insufficient progress in maximizing graft survival for Indigenous Australians. Indigenous Australians have at least 6 times the age-standardized incidence of end-stage kidney disease requiring renal replacement therapy as non-Indigenous Australians. Among adults aged 25 to 64 years and people from remote areas, rates are up to 15 times higher. Although constituting only 3.0% of the Australian population, over 1 in 10 patients commencing renal replacement therapy each year in Australia are Indigenous. At the end of 2015, 1647 (13.2%) of 12 461 patients receiving dialysis treatment in Australia were Indigenous; in contrast, only 241 (2.3%) of 10 551 patients with a functioning kidney transplant were Indigenous.2 Waitlisting for deceased donor kidney transplantation is uncommon for Indigenous patients. At the end of 2015, 1.9% of all Indigenous dialysis patients were on the waiting list, in contrast to 9.5% of non-Indigenous patients.2 This leads to lower transplantation rates. When all else is equal, Indigenous Australians have a quarter the chance of non-Indigenous patients of receiving a kidney transplant, with rates broadly like those in United States, Canada, and New Zealand.3 Indigenous patients understand the potential advantages of kidney transplantation and want access to this treatment modality, but concerns among kidney specialists about poorer outcomes for Indigenous Australian patients compared with non-Indigenous patients appear to be a major reason for nonreferral for deceased donor waitlisting.4 Posttransplantation outcomes for Indigenous Australians have indeed been worse than those for non-Indigenous Australians. Analysis of national registry data shows that Indigenous kidney transplant recipients, after adjustment for age and comorbidity, had almost twice the risk of death of Indigenous recipients between 2000 and 2012, and a 60% increased chance of losing a kidney transplant.5 Unlike non-Indigenous kidney transplant recipients (in whom cardiac and cancer causes predominate), and in contrast with widespread perceptions that immunosuppressive medication noncompliance is the major problem among Indigenous patients,4 infection has been the dominant cause of death or kidney transplant loss. However, in the presence of inequity in access to kidney transplantation, is it appropriate to adopt a predominantly utilitarian approach to decisions regarding waitlisting? Rather than comparing Indigenous and non-Indigenous transplant outcomes, it would appear fairer to compare the risks and benefits of transplant versus remaining on dialysis for Indigenous patients. National coordination to improve outcomes for Indigenous kidney transplant recipients (involving shared approaches to data collection, immunosuppression, monitoring, and infection prophylaxis for the small number currently transplanted) has been suggested by us as a stepping stone to improved access to the waiting list, but has proven challenging to implement. The Australian transplant community has been capable of achieving incremental but ultimately large improvements in outcome over the last 45 years. Without targeted efforts, Australians will continue to experience 2 tiers of end-stage kidney disease treatment outcomes: among the best in the world for non-Indigenous patients, and something substantially less than that for Indigenous Australians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.340
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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