Diagnosing Hypertension Among Adults; A Study Based on Prevention-Management of Primary and Secondary Hypertension
Bibliographic record
Abstract
Hypertension is a great challenge for the public health professionals across the world, as it is a major risk factor leading to congestive heart failure, coronary heart disease, retinopathy, and renal disease. Therefore, the study aims to diagnose hypertension among the adults in Al-Riyadh district, Khartoum state of Sudan. The study investigated the prevention and management of primary and secondary hypertension. A cross sectional population-based study was conducted among 138 adult individuals aged between 16 and 75 years. The participants were selected using random sampling technique and each participant completed self-administered questionnaire to assess the prevention-management of primary and secondary hypertension. The mercury sphygmomanometer with standard cuff was used to take measurement of arterial blood pressure. The diagnosis of hypertension among the adults showed that its prevalence in the area of Riyadh was 28%. The highest proportion of hypertension (13.7%) was recorded among the patients aged from 45 and 60 years. There was significant association of age (p-value = 0.001), social status (p-value = 0.001), stress (p-value = 0.010), and diabetes (p-value = 0.050) with hypertension. The present study has highlighted the significant factors associated with hypertension that encourages the public health professionals to carry out awareness and prevention programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".