Ethnic Background Is a Potential Barrier to Living Donor Kidney Transplantation in Canada
Bibliographic record
Abstract
BACKGROUND: We examined if African or Asian ethnicity was associated with lower access to kidney transplantation (KT) in a Canadian setting. METHODS: Patients referred for KT to the Toronto General Hospital from January 1, 2003, to December 31, 2012, who completed social work assessment, were included (n = 1769). The association between ethnicity and the time from referral to completion of KT evaluation or receipt of a KT were examined using Cox proportional hazards models. RESULTS: About 54% of the sample was white, 13% African, 11% East Asian, and 11% South Asian; 7% had "other" (n = 121) ethnic background. African Canadians (hazard ratio [HR], 0.75; 95% CI: 0.62-0.92]) and patients with "other" ethnicity (HR, 0.71; 95% CI, 0.55-0.92) were less likely to complete the KT evaluation compared with white Canadians, and this association remained statistically significant in multivariable adjusted models. Access to KT was significantly reduced for all ethnic groups assessed compared with white Canadians, and this was primarily driven by differences in access to living donor KT. The adjusted HRs for living donor KT were 0.35 (95% CI, 0.24-0.51), 0.27 (95% CI, 0.17-0.41), 0.43 (95% CI, 0.30-0.61), and 0.34 (95% CI, 0.20-0.56) for African, East or South Asian Canadians and for patients with "other" ethnic background, respectively. CONCLUSIONS: Similar to other jurisdictions, nonwhite patients face barriers to accessing KT in Canada. This inequity is very substantial for living donor KT. Further research is needed to identify if these inequities are due to potentially modifiable barriers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".