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Record W2051617591 · doi:10.1111/jch.12455

Psychological Distress and the Development of Hypertension Over 5 Years in Black South Africans

2014· article· en· W2051617591 on OpenAlexfundno aff
Aletta E. Schutte, Lisa J. Ware, Hugo W. Huisman, C.M.T. Fourie, Minrie Greeff, Tumi Khumalo, Marié P. Wissing

Bibliographic record

VenueJournal of Clinical Hypertension · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersNorth-West UniversityRoche DiagnosticsSouth Africa Netherlands research Programme on Alternatives in DevelopmentSouth African Medical Research CouncilPopulation Health Research Institute
KeywordsMedicineBlood pressurePsychological distressDistressAlcohol intakeHazard ratioAlcoholInternal medicinePsychiatryMental healthClinical psychologyConfidence interval

Abstract

fetched live from OpenAlex

Alarming increases in the incidence of hypertension in many low- and middle-income countries are related to alcohol overuse. It is unclear whether alcohol overuse is a symptom of psychological distress. The authors assessed psychological distress in Africans and its relationship with a 5-year change in blood pressure (BP), independent of alcohol intake. The authors followed 107 Africans with optimal BP (≤120/80 mm Hg) (aged 35-75 years) over 5 years. Alcohol intake (self-report and serum γ-glutamyl transferase) and nonspecific psychological distress (Kessler Screening Scale for Psychological Distress [K6]) were assessed. The K6 predicted hypertension development (P=.019), and its individual component "nervous" increased a participant's risk two-fold to become hypertensive (hazard ratio, 2.00 [1.23-3.26]). By entering K6 and γ-glutamyl transferase into multivariable-adjusted regression models for change in systolic BP, both were independently associated with change in systolic BP. Psychological distress and scoring high on being nervous predicted the development of hypertension over 5 years, independent of alcohol intake.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.064
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.208
GPT teacher head0.458
Teacher spread0.250 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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