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Record W2329224958 · doi:10.1097/hjh.0000000000000084

The blood pressure and hypertension experience among North American Indigenous populations

2014· review· en· W2329224958 on OpenAlexafffundabout
Heather J.A. Foulds, Darren E. R. Warburton

Bibliographic record

VenueJournal of Hypertension · 2014
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsIndigenousMedicineBlood pressureEthnic groupDemographyPopulationAfrican americanGerontologyInternal medicineEnvironmental healthEthnologyEcologyBiology

Abstract

fetched live from OpenAlex

Hypertension is becoming increasingly prevalent among western societies. However, different ethnic groups appear to be affected unequally. This systematic review sought to evaluate blood pressure and hypertension among North American Indigenous populations. Electronic databases (e.g. MEDLINE and EMBASE) were searched and citations cross-referenced. Articles including blood pressure or hypertension among Indigenous populations specifically were included. A total of 1213 unique articles were identified, with 141 included in the final review. Hypertension rates ranged from 19.2% among Inuit/Alaskan natives to 33.9% among First Nations/American Indians, and have increased since pre1980. Overall, hypertension rates were lower among Indigenous populations compared with general populations (23.5 vs. 31.2%), although average blood pressures were similar (123.3/75.1 vs. 124.9/75.2 mmHg). Limited information regarding Indigenous children/youth identified 11.4% hypertension rates, with average blood pressures of 106.7/60.2 mmHg. These findings indicate that current rates of hypertension may actually be lower among Indigenous populations than the general population.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.065
GPT teacher head0.314
Teacher spread0.249 · 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 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

Citations32
Published2014
Admission routes3
Has abstractyes

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