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Record W2905864418 · doi:10.5539/gjhs.v11n1p150

Elevated Blood Pressure of High Altitude Dwelling Andibila Adults in Oju, Nigeria

2018· article· en· W2905864418 on OpenAlexvenueno aff
Daniel Ter Goon, Charles Mpofu, Vincent Oladele Adeniyi, Uchenna Benedine Okafor, Simon Wuhe Akusu, Benjamin Ijuo Ejeh, Unogwu O Unogwu

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsnot available
Fundersnot available
KeywordsBlood pressureMedicineWaistEffects of high altitude on humansAltitude (triangle)DiastoleInternal medicineAnthropometryDemographyObesityGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the blood pressure of high altitude dwelling Andibila adults in Oju, Nigeria. METHODS: A cross-sectional survey of 121 Andibila adults living at a high altitude in Oju, Benue state, Nigeria. Body mass, stature, girths (waist and hip circumferences) were taken using standard procedures. Blood pressure (BP) measurement was assessed twice, and the average recorded. Participants with a systolic blood pressure (SBP) of ≥ 140 and diastolic blood pressure (DBP) of ≥ 90 were diagnosed as hypertensive. RESULTS: The participants mean age was 49.9 years (SD=16.5. The prevalence rate of elevated pressure was 55.9%. Traditional alcohol use was significantly common in males (49.1%; OR=31.8; 95% CI 7.1 –143.3; p<0.0001) than females (2.9%). Increase in SBP was significantly associated with increasing age (r=0.198; p=0.0301), WC (r=0.215; p=0.018) and BMI (r=0.242; p=0.008). CONCLUSION: There is need for health education and awareness campaign concerning the risk of elevated blood pressure of lean Andibila adults living in a geographically secluded setting.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.289
Teacher spread0.280 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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