Increasing Prevalence of Hypertension in Bangladesh: A review
Bibliographic record
Abstract
Hypertension is a major public health problem globally in both the developed and developing countries. In Bangladesh, approximately 20% of adult and 4065% of elderly people suffer from hypertension. According to Non Communicable Disease risk factors survey, one third of the Bangladeshi population never measured their blood pressure. The prevalence of self reported hypertension was 12.5% (men 10.9% and women 13.9%). Many risk factors underlying hypertension have been identified including non- modifiable factors such as age, gender, genetic factors, and race, as well as modifiable factors including overweight, high sodium intake, and reduced physical activity. The aim of this review was to present the scenery of hypertension and as well as present the exponential trend of hypertension in Bangladesh. We included 9 studies for the review that met the inclusion criteria and study objectives. Analyses of exponential trend reveled an increase in hypertension prevalence among adult population at a rate of 0.04 (R=0.33) per year. This review studies have demonstrated that interventions aimed at changing these modifiable factors might contribute to prevent the development of hypertension.Cardiovasc. j. 2015; 8(1): 59-64
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".