Prevalence and determinants of hypertension in the Algerian Sahara
Bibliographic record
Abstract
BACKGROUND: In-Salah is a city-oasis located in the middle of the Algerian Sahara, a desert area whose drinking water has a high sodium content. No cardiovascular epidemiological studies have ever been conducted in this region. METHODS: A randomized sample of 635 men and 711 women, aged 40-99 years, was studied. Blood pressure measurements, combined with a clinical questionnaire that included educational and socio-economic data, and standard blood samples for the detection of dyslipidemia and diabetes mellitus, were collected. RESULTS: The mean age was 55 +/- 12 years. The prevalence of hypertension was 44% and was highly influenced by age, sex, skin colour, educational status, obesity and metabolic parameters. The higher prevalence of hypertension among black individuals was independent of socio-economic and educational levels, and of metabolic parameters. The presence of antihypertensive treatment was three times more frequent in women than in men, and there was no difference according to skin colour. Among treated subjects, 25% were well controlled, and this percentage was similar among both black and white individuals. CONCLUSION: Epidemiological studies in such an emergent population indicate that hypertension is a major public health problem. The high sodium content in drinking water in this region could play a major role in the development of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".