Planting the Seed for Blood Pressure Control: The Role of Plant-Based Nutrition in the Management of Hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: Hypertension results in significant morbidity, mortality, and healthcare expenditures. Fortunately, it is largely preventable and treatable by implementing dietary interventions, though these remain underutilized. Here, we aim to explore the role of healthy dietary patterns in hypertension management and describe approaches for busy clinicians to address nutrition effectively and efficiently with patients. RECENT FINDINGS: DASH, Mediterranean, vegetarian, and vegan diets that include minimally processed, plant-based foods as core elements have consistently shown positive effects on hypertension. Recommendations that distill the most healthful components of these diets can significantly impact patient outcomes. Clinicians can harness evidence-based dietary assessment and counseling tools to implement and support behavioral changes, even during brief office visits. Healthful plant-based dietary patterns can often effectively prevent and treat hypertension. Clinicians may help improve patient outcomes by discussing evidence-based nutrition with their patients. Future work to promote infrastructural change that supports incorporating evidence-based nutrition into medical education, clinical care, and society at large can support these efforts.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".