Examining Inequities in Clinical Outcomes for Indigenous Patients Treated With Dialysis in Canada: A Scoping Review
Bibliographic record
Abstract
The burden of kidney disease among Indigenous peoples in Canada is disproportionately higher than the rest of the population. We aimed to synthesize the existing knowledge on the clinical outcomes among Indigenous peoples treated with dialysis in Canada. We searched MEDLINE, Embase, CINAHL, Scopus, Web of Science Core Collection, and the Bibliography of Indigenous Peoples of North America, supplemented by a gray literature review. The following inclusion criteria were used: (1) studies assessing dialysis patients, (2) including Canadian Indigenous patients, and (3) relating to incidence, mortality, treatment complications, access to care, and/or quality of life. Forty-four studies, conducted across multiple Canadian provinces, were included. Fifteen studies highlighted the higher prevalence of diabetic and nondiabetic kidney failure among Indigenous Canadians compared with non-Indigenous Canadians. Indigenous patients experienced more frequent dialysis-related infections and cardiovascular complications, increased hospitalization rates, lower rates of arteriovenous fistula creation, lower use of home dialysis, reduced access to health care services, and decreased quality of life because of relocation for dialysis. Few studies explored the underlying causes of the observed inequities. Our findings underscore the need to better understand the contributing factors to develop culturally appropriate interventions, codesigned with Indigenous communities, that promote equitable care for Indigenous patients receiving dialysis.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".