Nutrition Users’ Guides: an introduction to structured guides to evaluate the nutrition literature
Bibliographic record
Abstract
Despite evidence that nutrition can play a substantial role in curbing the burden of chronic disease, findings reported in the nutrition literature have been plagued with debate and uncertainty, including questions about the confidence we can place in evidence from observational studies, the validity of dietary intake data, and the applicability of randomised trials to real-world patients or members of the public. Structured nutrition users' guides (NUGs) to evaluate common research study designs (ie, randomised trials, cohort studies, systematic reviews and clinical practice guidelines) addressing nutrition questions will help clinicians and their patients, as well as health service workers and policy-makers, use the evidence to make more informed decisions on disease management and prevention. In addition, NUGs will provide comprehensive teaching materials for nutrition trainees on how to appraise, interpret and apply the research evidence. We hereby introduce a series of structured NUGs for the literature on nutrients, foods and dietary patterns and programmes. Each article will address three key components when assessing different study designs used to assess nutrition interventions or exposures, including (1) assessing the methodological quality of the study, (2) interpreting study results (magnitude and precision of treatment or exposure effects for outcomes of benefit and harm) and (3) applying the results to unique patient or population scenarios based on their health-related values and preferences related to the potential benefits, harms, convenience and cost of an intervention. This series of articles will serve to empower clinicians, health service workers and health policy-makers to better understand the validity, interpretability and applicability of the nutrition literature, while also helping practitioners and their clients make more evidence-based, value-sensitive and preference-sensitive nutrition decisions.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".