Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and risk of total and cause-specific mortality: results from the Golestan Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and overall and cause-specific mortality in the Golestan Cohort Study (GCS). METHODS: A total of 50 045 participants aged 40 years or older were recruited from Golestan Province, Iran, from 2004 to 2008 and followed for a mean of 10.64 years. The DASH diet score was calculated for each individual based on food groups. The primary outcome measure was death from any cause. RESULTS: During 517 326 person-years of follow-up, 6763 deaths were reported. After adjustment for potential confounders, DASH diet score was inversely associated with risk of death from all causes and cancers [hazard ratio (HR): 0.86; 95% confidence interval (CI): 0.75, 0.98; and HR: 0.65; 95% CI: 0.47, 0.90, respectively]. A higher DASH diet score was associated with lower risk of gastrointestinal cancer mortality in men (HR: 0.55; 95% CI: 0.30, 0.99). A greater adherence to DASH diet was also associated with lower other-cancer mortality in women (HR: 0.50; 95% CI: 0.24, 0.99). No association between DASH diet score and cardiovascular disease mortality was observed, except that those dying of cardiovascular disease were younger than 50 years of age and smokers. CONCLUSIONS: Our findings suggest that maintaining a diet similar to the DASH diet is independently associated with reducing the risk of total death, cancers, and especially gastrointestinal cancers in men.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".