Association of adherence to the dietary approach to stop hypertension diet and diet quality indices among women in Tehran: A cross sectional study
Bibliographic record
Abstract
Background: Examining dietary approach to stop hypertension (DASH) diet based on other dietary quality indices can be helpful to clarify positive aspects of this healthy dietary pattern. We aimed to examine the association between the DASH diet score and some diet quality indices among Iranian women. Methods: In this cross-sectional study, 304 women aged 20 to 50 years old were recruited. Dietary diversity score (DDS), dietary energy density (DED), adherence to DASH diet, AlternativeHealthy Eating Index (AHEI) and mean adequacy ratio (MAR) were examined as suggested by previous articles. Dietary quality indices, anthropometric indices, and dietary intake were categorized based on DASH tertiles. A semi-quantitative food frequency questionnaire with 168items was used for dietary assessment. Results: There were no significant differences in the demographic characteristics of participants across DASH tertiles (P>0.05). Participants who adhered more to the DASH diet had lower DEDthan those with lower adherence (0.99±0.35 vs 1.26±0.30; P=0.01). Significant differences were observed in the index of DDS across tertiles (P=0.01), however no differences in nutrient adequacy ratio (NAR) and MAR (0.93) index across the DASH categories were found.Additionally, DDS to DED in the top tertile of the DASH diet was greater than the bottom one(6.7±2.9 vs 4.4±1.9; P=0.001). Conclusion: The present study indicated that greater adherence to the DASH diet is inversely associated with DED and AHEI. As well as, there was a positive association between the DASHdiet and DDS/DED ratio. However, more studies are needed to confirm the results of this study.
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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 | 0.000 |
| 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".