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Record W4414299580 · doi:10.1016/j.xkme.2025.101106

Examining Inequities in Clinical Outcomes for Indigenous Patients Treated With Dialysis in Canada: A Scoping Review

2025· review· en· W4414299580 on OpenAlexafffundabout
Noémie Laurier, Taylor Stoesz, Andrea Quaiattini, Caitlin Gilpin, Romina Pace, Laura Horowitz, Rita S. Suri, Shaifali Sandal, Emilie Trinh

Bibliographic record

VenueKidney Medicine · 2025
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsMontreal General HospitalCree Board of Health and Social Services of James BayMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsIndigenousDialysisRelocationKidney diseaseHealth careDiseasePublic health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.550
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.421
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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