Long-Term Medical Outcomes Among Aboriginal Living Kidney Donors
Bibliographic record
Abstract
BACKGROUND: It is unknown whether favorable long-term data on the safety of living kidney donation can be extrapolated to populations at higher risk of chronic kidney disease. Indigenous people (i.e., Aboriginals) have a high prevalence of risk factors for chronic kidney disease and Aboriginal living donor outcomes need to be defined. METHODS: We performed a retrospective cohort study of all 38 Aboriginal donors donating at our center since 1970 and 76 randomly selected white donor controls to determine the long-term rates of hypertension, diabetes, and renal function postdonation. RESULTS: Follow-up was obtained for 91% of both Aboriginal and white donors (mean follow-up approximately 14 years). Hypertension has been diagnosed more frequently among Aboriginal donors (Ab 42% vs. white 19%, P=0.02). Notably, all 11 Aboriginal donors more than 20 years postdonation have developed hypertension. Diabetes has also been diagnosed more frequently among Aboriginal donors (Ab 19% vs. white 2%, P=0.005), including 5 of 11 (45%) more than 20 years postdonation. Follow-up estimated glomerular filtration rate was higher in Aboriginal donors (Ab 77+/-17 vs. white 67+/-13 mL/min/1.73 m, P=0.002) but not significantly different in adjusted analyses. One Aboriginal donor developed end-stage renal disease 14 years postdonation. CONCLUSIONS: Aboriginal living kidney donors at our center have high rates of hypertension and diabetes on long-term follow-up, although renal function is preserved to date. This profile is similar to that of the general unselected Aboriginal population despite detailed medical evaluation before donation. These findings have important implications for donor counseling and may have implications for other high-risk donor populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".