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Record W2610795788 · doi:10.1186/s40695-017-0020-z

Gender disparities in midlife hypertension: a review of the evidence on the Arab region

2017· review· en· W2610795788 on OpenAlexfundno aff
Christelle Akl, Chaza Akik, Hala Ghattas, Carla Makhlouf Obermeyer

Bibliographic record

VenueWomen s Midlife Health · 2017
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
FundersInternational Development Research CentreUtah Agricultural Experiment Station
KeywordsGerontologyDemographyMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

While gender differences in hypertension and increased prevalence rates among women at midlife have been documented in multiple settings, the evidence on the Arab world has not been systematically examined. This review summarizes the evidence related to gender disparities in midlife hypertension in this region. We searched MEDLINE and Social Sciences Citation Index (SSCI) databases for studies, published between January 2000 and August 2015, on hypertension in the 22 countries of the Arab region. We abstracted information on the prevalence of hypertension among women and men, in general populations during midlife. Nineteen studies provided data on the prevalence of hypertension by gender and age in the Arab world. Higher rates of hypertension were found among Arab women at midlife in most countries. In studies that included subjects younger than 35 years old, a decrease in sex ratios (M/F) at midlife was observed in all countries except Palestine. Higher female prevalence rates are observed in the 4th decade of life in most countries of the region, almost two decades earlier than in other parts of the world. This review highlights the need for more systematic examinations of hypertension in the Arab region, its risk factors, and the reasons for the particular patterns of gender differences that are observed. Such research would have considerable implications for prevention, treatment, and improved well-being.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.469
GPT teacher head0.432
Teacher spread0.036 · 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.

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

Citations23
Published2017
Admission routes1
Has abstractyes

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