Evidence Collection and Evaluation for the Development of Dietary Guidelines and Public Policy on Nutrition
Bibliographic record
Abstract
Dietary guidelines and recommendations, usually developed by government bodies or large authoritative organizations, have major downstream effects on public policy. A growing body of evidence supports the notion that there are serious deficiencies in the methods used to develop dietary guidelines. Such deficiencies include the failure to access or conduct comprehensive systematic reviews, a lack of systematic or rigorous evaluation of the quality of the evidence, a failure to acknowledge the limitations of the evidence base underlying recommendations, and insufficiently stringent management of conflicts of interest. These issues may be addressed by adhering to international standards for guideline development, including adopting systematic review methodology and using rigorous systems to evaluate the certainty of the evidence and to move from evidence to recommendations, of which the GRADE approach (Grading of Recommendations Assessment,Development and Evaluation) is the most rigorous and fully developed. Improving the methods by which dietary guidelines are produced has considerable potential to substantially improve public policy decision-making.
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 | 0.317 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".