Knowledge About Renal Transplantation Among African, Caribbean, and Black Canadian Patients With Advanced Kidney Failure
Bibliographic record
Abstract
Introduction: Variable transplant-related knowledge may contribute to inequitable access to living donor kidney transplant (LDKT). We compared transplant-related knowledge between African, Caribbean, and Black (ACB) versus White Canadian patients with kidney failure using the Knowledge Assessment of Renal Transplantation (KART) questionnaire. Methods: This was a cross-sectional cohort study. Data were collected from a cross-sectional convenience sample of adults with kidney failure in Toronto. Participants also answered an exploratory question about their distrust in the kidney allocation system. Clinical characteristics were abstracted from medical records. The potential contribution of distrust to differences in transplant knowledge was assessed in mediation analysis. Results: Among 577 participants (mean [SD] age 57 [14] years, 63% male), 25% were ACB, and 43% were White Canadians. 45% of ACB versus 26% of White participants scored in the lowest tertile of the KART score. The relative risk ratio to be in the lowest tertile for ACB compared to White participants was 2.22 (95% confidence interval [CI]: 1.11, 4.43) after multivariable adjustment. About half of the difference in the knowledge score between ACB versus White patients was mediated by distrust in the kidney allocation system. Conclusion: Participants with kidney failure from ACB communities have less transplant-related knowledge compared to White participants. Distrust is potentially contributing to this difference.
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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 | 0.000 |
| 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".