Primary prevention of CVD: modification of diet in people with hypertension.
Bibliographic record
Abstract
INTRODUCTION: Hypertension (persistent diastolic blood pressure of 90 mmHg or greater or systolic blood pressure 140 mmHg or greater) affects 20% to 35% of the world's adult population and increases the risk of cardiovascular disease, end-stage renal disease, and mortality. METHODS AND OUTCOMES: We conducted a systematic overview, aiming to answer the following clinical question: What are the effects of selected dietary modification for people with hypertension? We searched: Medline, Embase, The Cochrane Library, and other important databases up to October 2013 (Clinical Evidence overviews are updated periodically; please check our website for the most up-to-date version of this overview). RESULTS: At this update, searching of electronic databases retrieved 669 studies. After deduplication and removal of conference abstracts, 464 records were screened for inclusion in this overview. Appraisal of titles and abstracts led to the exclusion of 376 studies and the further review of 88 full publications. Of the 88 full articles evaluated, three systematic reviews and three RCTs were added. We performed a GRADE evaluation for eight PICO combinations. CONCLUSIONS: In this systematic overview, we categorised the efficacy for five interventions based on information about the effectiveness and safety of calcium supplements, a low-salt diet (including the DASH diet), magnesium supplements, a Mediterranean diet, and potassium supplements.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".