A Systematic Review and Recommendations Around Frameworks for Evaluating Scientific Validity in Nutritional Genomics
Bibliographic record
Abstract
Background: There is a significant lack of consistency used to determine the scientific validity of nutrigenetic research. The aims of this study were to examine existing frameworks used for determining scientific validity in nutrition and/or genetics and to determine which framework would be most appropriate to evaluate scientific validity in nutrigenetics in the future. Methods: A systematic review (PROSPERO registration: CRD42021261948) was conducted up until July 2021 using Medline, Embase, and Web of Science, with articles screened in duplicate. Gray literature searches were also conducted (June-July 2021), and reference lists of two relevant review articles were screened. Included articles provided the complete methods for a framework that has been used to evaluate scientific validity in nutrition and/or genetics. Articles were excluded if they provided a framework for evaluating health services/systems more broadly. Citing articles of the included articles were then screened in Google Scholar to determine if the framework had been used in nutrition or genetics, or both; frameworks that had not were excluded. Summary tables were piloted in duplicate and revised accordingly prior to synthesizing all included articles. Frameworks were critically appraised for their applicability to nutrigenetic scientific validity assessment using a predetermined categorization matrix, which included key factors deemed important by an expert panel for assessing scientific validity in nutrigenetics. Results: Upon screening 3,931 articles, a total of 49 articles representing 41 total frameworks, were included in the final analysis (19 used in genetics, 9 used in nutrition, and 13 used in both). Factors deemed important for evaluating nutrigenetic evidence related to study design and quality, generalizability, directness, consistency, precision, confounding, effect size, biological plausibility, publication/funding bias, allele and nutrient dose-response, and summary levels of evidence. Frameworks varied in the components of their scientific validity assessment, with most assessing study quality. Consideration of biological plausibility was more common in frameworks used in genetics. Dose-response effects were rarely considered. Two included frameworks incorporated all but one predetermined key factor important for nutrigenetic scientific validity assessment. Discussion/Conclusions: A single existing framework was highlighted as optimal for the rigorous evaluation of scientific validity in nutritional genomics, and minor modifications are proposed to strengthen it further. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=261948 , PROSPERO [CRD42021261948].
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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 | 0.653 | 0.824 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.069 | 0.042 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.020 | 0.022 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".