Methods for trustworthy nutritional recommendations NutriRECS (Nutritional Recommendations and accessible Evidence summaries Composed of Systematic reviews): a protocol
Bibliographic record
Abstract
BACKGROUND: Recent systematic reviews and editorials suggest that many organizations that produce nutritional guideline recommendations do not adhere to internationally recognized standards set forth by the Institute of Medicine (IoM), Guidelines International Network (GIN), Appraisal of Guidelines Research and Evaluation (AGREE), and Grading Recommendations, Assessment, Development and Evaluation (GRADE). METHODS: The potential solution is an independent group with content expertise and skilled in the methodology of systematic reviews and practice guidelines to produce trustworthy guideline recommendations, recommendations that are supported by publication in a top tier journal. The BMJ Rapid Recommendations project has recently demonstrated the feasibility and utility of this approach. Here, we are proposing trustworthy nutritional guideline recommendations based on internationally accepted guideline development standards, recommendations that will be informed by rigorous and novel systematic reviews of the benefits and harms associated with nutritional exposures, as well as studies on the values and preferences related to dietary behaviors among members of the international community. DISCUSSION: Adhering to international guideline standards, conducting high quality systematic reviews, and actively assessing the values and preferences of key stakeholders is expected to improve the quality of nutritional guidelines and their relevance to end-users, particularly patients and community members. We will send our work for peer review, and if found acceptable, we will publish our nutritional recommendations in top-tier general medicine journals.
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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 | 0.412 | 0.562 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.106 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".