EFFECT OF VARIOUS DIETARY PATTERN ON BLOOD PRESSURE MANAGEMENT: A REVIEW
Bibliographic record
Abstract
Hypertension is a major contributor to the burden of cardiovascular morbidity and mortality worldwide as 25% of the world’s adult population suffers from hypertension. For people with cardiovascular disease, adherence to a healthy diet has benefits additive to drug therapy and associated with reduction in mortality of between 8 and 45%. This review focuses the effect of various dietary patterns on blood pressure management. General search of academic journals (English) on diet, dietary pattern, blood pressure, hypertension and risk published from 2010 to 2019 was conducted. A total of 20 studies were selected from two electronic databases (PubMed, Science Direct). Eleven of the studies were from United States, one from Canada, China, Italy, Australia, Denmark, Spain, Germany, Brazil and Netherland. Twenty studies showed reduction in Systolic Blood Pressure (SBP) which range from 0.60 mmHg to 20.79 mm Hg and the highest reduction in SBP was from combination of Dietary Approach to Stop Hypertension (DASH) diet with low sodium intake. Diastolic Blood Pressure (DBP) nitrate-rich vegetable diet indicated highest reduction where it ranges from 0.60 mm Hg to 9.00 mm Hg. Fruits and vegetables intake should be practice to prevent the burden of non-communicable disease. This is great importance to public health and to reduce medical costs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| 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".