Access to living donor transplantation for Aboriginal recipients: a study of living donor presentation and exclusion
Bibliographic record
Abstract
Aboriginals experience high rates of end-stage renal disease (ESRD) and are less likely to receive a kidney transplant from a living donor. We hypothesized that higher rates of hypertension and diabetes in Aboriginal communities would result in fewer potential living donors coming forward and more exclusions for medical reasons. We performed a retrospective study to examine the frequency of potential donor presentation and the reasons for donor exclusion among Aboriginal and Caucasian wait-listed ESRD patients at our center. Three hundred and eighty-five wait-listed patients were studied, including 174 Aboriginals and 211 Caucasians. Time on the waiting list was similar between groups. A similar proportion of Aboriginals and Caucasians had at least one potential donor (40% vs. 46%), and the rate of donor exclusion for medical reasons was also similar (23% vs. 21%). Potential donors to Aboriginals were more likely to be excluded for non-medical reasons (50% vs. 30%; p < 0.0001), of which 96% were because of loss of contact. Waitlisted Aboriginal ESRD patients appear just as likely as Caucasians to have potential living donors initiate evaluation and have a similar rate of donor exclusion because of medical reasons. Further work is required to identify why donors to Aboriginals are more likely to withdraw from the evaluation process.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".