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Record W2674269189

Hypertension and obesity among HIV patients in a care programme in Nairobi

2017· article· en· W2674269189 on OpenAlexaboutno aff
Martin Masika, Charles Wachihi, Festus Muriuki, John Karani Kimani

Bibliographic record

VenueEast African Medical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOverweightObesityPopulationPsychological interventionPediatricsBlood pressureInternal medicineEnvironmental healthNursing
DOInot available

Abstract

fetched live from OpenAlex

Objective: To determine the prevalence of hypertension and obesity among HIV patients enrolled in the Sex Worker Outreach Programme (SWOP), Nairobi, Kenya. Design: A retrospective a study. Setting: SWOP managed by the University of Manitoba, Nairobi team. Subjects: We selected clinic visit records from HIV patients seen between 2011 and 2014, which had valid blood pressure and age entries. Interventions: We analysed data to determine prevalence and correlates of hypertension and obesity in the study population. Associations were tested using chi-square for categorical variables and t-test for continuous variables. Main outcome measures: Hypertension and obesity. Results: Three thousand one hundred ninety seven subjects were included in the study. All were HIV-positive and most (97.8%) were on ART. The mean age was 39.7 years (standard deviation = 8.8) and 72.4% of the subjects were female. The prevalence of hypertension was 7.7% (246/3197) and 31% of the study cases (798/2590) were either overweight or obese. Males were more likely to have hypertension (p < 0.001) while females were more predisposed to obesity (p < 0.001). Conclusion: Hypertension and obesity are important co-morbidities among HIV patients. Preventive and management strategies should be adopted as part of the comprehensive packages on offer at all existing HIV care and ART centres targeting those enrolled for services as well as their relatives and the community at large.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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