Factors associated with hypertension among men in Darkhan-Uul Province, Mongolia: A cross-sectional study
Bibliographic record
Abstract
In Mongolia, cardiovascular disease is the leading cause of death, and prevalence of hypertension is very high. The aim of this study was to investigate the association between hypertension and sociodemographic factors and health-related behaviors among men in Darkhan-Uul Province, Mongolia. This cross-sectional study was conducted between November 2015 and January 2016. Men whose wives were pregnant with ≤ 20 weeks gestation and had attended antenatal health checkups at public health facilities were recruited in this study. The data were collected as part of a survey of pregnant women and their partners. Data were collected using self-administered questionnaires, anthropometry, and spot urine samples. A total of 224 men participated in the survey, and data from 209 participants were included in the analysis. Multiple logistic regression analysis showed that men with higher BMI had higher odds of hypertension than those with lower BMI (adjusted odds ratio [AOR]: 1.14, 95% CI: 1.03-1.26). Those with urinary cotinine > 100 ng/ml (smokers) had a lower risk of hypertension (AOR: 0.24, 95% CI: 0.09-0.67) compared to participants with urinary cotinine/ml (nonsmokers). This is the first study to investigate the association between hypertension and sociodemographic factors and health-related behaviors among men in Mongolia. Based on the findings of this study, clinicians responsible for public health in Mongolia should provide health education regarding the importance of weight control in preventing hypertension.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".