Strengthening the Reporting of Nutritional Genomics Research to Inform Knowledge Translation in Personalized Nutrition
Bibliographic record
Abstract
The ultimate goal of researching nutrigenetic interactions is to be able to provide individuals with genetically-tailored nutrition advice (when evidence is sufficient) in an effort to optimize health outcomes. Accordingly, original research often discusses the potential for the results to inform genetically-tailored nutrition advice. Despite this, many studies do not report their methods, results, and discussion in a manner that is conducive to knowledge translation. With several consumer nutritional genomics companies now offering genetic testing for personalized nutrition, proper reporting of nutritional genomics research for knowledge translation is of vital importance. Common reporting errors relate to SNP and genotype reporting, results lacking detail, consideration of linkage disequilibrium, mechanisms of action/functional SNPs, details of dietary intake, and sample reporting. Because of this, knowledge translation professionals may be unable or challenged in their attempt to use the findings from such research to inform clinical practice in nutritional genomics and personalized nutrition. The present article provides an overview of the issues at hand. It further pre-sents a checklist as well as table and figure templates for researchers to use when reporting the results of original research in nutritional genomics to inform knowledge translation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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 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".