Development of a Concise Healthy Diet Score for Cardiovascular Disease among Japanese; The Japan Collaborative Cohort Study
Bibliographic record
Abstract
AIMS: Several diet quality indicators have been developed primarily for cardiovascular disease (CVD) prevention in Western countries. However, those previous indicators are complicated and less feasible in clinical and health-promoting settings. Therefore, we aimed to develop a concise dietary risk score for CVD prevention in Japanese. METHODS: Using the self-administered food frequency questionnaire with 35 food items, we developed a concise healthy diet score (cHDS) ranging from 0 to 5 points. We examined the association of cHDS with risks of all-cause and cause-specific mortality among 23,115 men and 35,557 women who were free of CVD and cancer. RESULTS: During 19.2 years of median follow-up, 6,291 men and 5,365 women died. In men, the multivariable hazard ratios (95% confidence intervals) for the highest cHDS (5 points) compared to the lowest (0-1 points) were 0.74 (0.60-0.91, P-trend=0.008) for CVD and 0.86 (0.77-0.95, P-trend=0.05) for all causes. No significant associations were found for stroke, coronary heart disease, and other causes in men. The corresponding hazard ratio in women was 0.65 (0.52-0.81, P-trend＜0.001) for CVD, 0.63 (0.45-0.88, P-trend＜0.001) for stroke, 0.48 (0.30-0.78, P-trend=0.008) for coronary heart disease, 0.67 (0.54-0.84, P-trend＜0.001) for other causes, and 0.75 (0.66-0.85, P-trend＜0.001) for all causes. CONCLUSION: We developed a concise diet quality score named cHDS in the Japanese population and found the inverse association of cHDS with mortality from CVD and all causes for both men and women.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".