Dietary patterns: biomarkers and chronic disease riskThis paper is one of a selection of papers published in the CSCN–CSNS 2009 Conference, entitled Are dietary patterns the best way to make nutrition recommendations for chronic disease prevention?
Bibliographic record
Abstract
With increasing appreciation of the complexity of diets consumed by free-living individuals, there is interest in the assessment of the overall diet or dietary patterns in which multiple related dietary characteristics are considered as a single exposure. The 2 most frequently used methods to derive dietary patterns use (i) scores or indexes based on prevailing hypotheses about the role of dietary factors in disease prevention; and (ii) factors and clusters from exploration of available dietary data. A third method, a hybrid of the hypothesis-driven and data-driven methods, attempts to predict food combinations related to nutrients or biomarkers with hypothesized associations with particular health outcomes. Dietary patterns derived from the first 2 approaches have been examined in relation to nutritional and disease biomarkers and various health outcomes, and generally show the desirable dietary pattern to be consistent with prevalent beliefs about what constitutes a healthful diet. Results from observational studies suggest that the healthful dietary patterns were associated with significant but modest risk reduction (15%-30%) for all-cause mortality and coronary heart disease. Findings for various cancers have been inconsistent. The available randomized controlled intervention trials with a long-term follow-up to examine dietary patterns in relation to health outcome have generally produced null findings. Novel findings with the potential to change existing beliefs about diet and health relationships are yet to emerge from the dietary patterns research. The field requires innovation in methods to derive dietary patterns, validation of prevalent methods, and assessment of the effect of dietary measurement error on dietary patterns.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".