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Record W2891012942 · doi:10.1177/2054358118799689

A Retrospective Study of Chronic Kidney Disease Burden in Saskatchewan’s First Nations People

2018· article· en· W2891012942 on OpenAlexaffabout
Dorothy Thomas, Anne Huang, Michelle McCarron, Joanne Kappel, Rachel M. Holden, Karen Yeates, Bonnie Richardson

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSaskatchewan HealthSaskatchewan Health AuthorityHealth CanadaQueen's University
Fundersnot available
KeywordsKidney diseaseMedicineVeterans AffairsHealth careMedical recordFamily medicineDiseaseRetrospective cohort studyGerontologyDemographyInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

Background: Chronic kidney disease is more prevalent among First Nations people than in non-First Nations people. Emerging research suggests that First Nations people are subject to greater disease burden than non-First Nations people. Objective: We aimed to identify the severity of chronic kidney disease and quantify the geographical challenges of obtaining kidney care by Saskatchewan’s First Nations people. Design: This study is a retrospective analysis of the provincial electronic medical record clinical database from January 2012 to December 2013. Setting: The setting involved patients followed by the Saskatchewan provincial chronic kidney care program, run out of two clinics, one in Regina, SK, and one in Saskatoon, SK. Patients: The patients included 2478 individuals (379 First Nations and 2099 non-First Nations) who were older than 18 years old, resident in Saskatchewan, and followed by the provincial chronic kidney care program. First Nations individuals were identified by their Indigenous and Northern Affairs Canada (INAC) Number. Measurements: The demographics, prevalence, cause of end-stage renal disease, severity of chronic kidney disease, use of home-based therapies, and distance traveled for care among patients are reported. Methods: Data were extracted from the clinical database used for direct patient care (the provincial electronic medical record database for the chronic kidney care program), which is prospectively managed by the health care staff. Actual distance traveled by road for each patient was estimated by a Geographic Information System Analyst in the First Nations and Inuit Health Branch of Health Canada. Results: Compared with non-First Nations, First Nations demonstrate a higher proportion of end-stage renal disease (First Nations = 33.0% vs non-First Nations = 21.4%, P < .001), earlier onset of chronic kidney disease (M FN = 56.4 years, SD = 15.1; M NFN = 70.6 years, SD = 14.7, P < .001), and higher rates of end-stage renal disease secondary to type 2 diabetes (First Nations = 66.1% vs non-First Nations = 39.0%, P < .001). First Nations people are also more likely to be on dialysis (First Nations = 69.7% vs non-First Nations = 40.2%, P < .001), use home-based therapies less frequently (First Nations = 16.2% vs non-First Nations = 25.7%; P = 003), and must travel farther for treatment ( P < .001), with First Nations being more likely than non-First Nations to have to travel greater than 200 km. Limitations: Patients who are followed by their primary care provider or solely through their nephrologist’s office for their chronic kidney disease would not be included in this study. Patients who self-identify as Aboriginal or Indigenous without an INAC number would not be captured in the First Nations cohort. Conclusions: In Saskatchewan, First Nations’ burden of chronic kidney disease reveals higher severity, utilization of fewer home-based therapies, and longer travel distances than their non-First Nations counterparts. More research is required to identify innovative solutions within First Nations partnering communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.602

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.271
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2018
Admission routes2
Has abstractyes

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