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Record W2003980226 · doi:10.3747/pdi.2010.00228

An International Perspective on Peritoneal Dialysis among Indigenous Patients

2011· review· en· W2003980226 on OpenAlexaffabout
Suma Prakash

Bibliographic record

VenuePeritoneal Dialysis International · 2011
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousPeritoneal dialysisMedicineSpecialtyReferralRuralityEthnic groupMEDLINEDemographyFamily medicineInternal medicineRural areaPolitical sciencePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To review utilization rates, outcomes, and barriers to peritoneal dialysis (PD) in indigenous peoples from an international perspective. METHODS: Articles were obtained from Medline and EMBASE and from author name and reference searches. Data from census bureaus and renal registries in Australia, Canada, New Zealand, and the United States were used. Studies were included if they contained information on utilization of, outcomes of, or barriers to PD in indigenous populations. RESULTS: In 2007, of all prevalent PD patients, 7.0%, 5.1%, 28.2%, and 1.3% in Australia, Canada, New Zealand, and the United States respectively were of indigenous background. The proportions of prevalent renal replacement therapy patients on PD reflected the national rates-New Zealand being the highest at 0.29, and the United States the lowest at 0.05. Mortality was generally higher in indigenous than in non-indigenous PD patients. Variations in mortality study results likely reflect differences in the definitions of explanatory variables such as rurality and in the availability of local specialty care services. Technique failure and peritonitis rates were higher among indigenous than among non-indigenous patients. CONCLUSIONS: The less favorable outcomes in indigenous PD patients across countries may, in part, be a manifestation of reduced access to resources. Understanding the effects of socio-economic, geographic, cultural, and language issues, and of health literacy discrepancies on various aspects of PD education, training, and outcomes can potentially identify ways in which outcomes might be improved among indigenous patients on PD.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.343
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations5
Published2011
Admission routes2
Has abstractyes

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