What Will It Take to Build an Expert Group of Nutrigenomic Practitioners?
Bibliographic record
Abstract
BACKGROUND: The past two decades have seen exponential growth in the number of genetic testing companies, but only a small percentage of these tests are being sold through health care professionals (HCPs). As each new genetic testing company appears, it is becoming more difficult for the practitioner and consumer to evaluate the credibility of the claims being made and the value of the tests being offered. SUMMARY: HCPs appear to have minimal nutrigenomics knowledge and little confidence in choosing and interpreting nutrigenetic tests. To remedy this, HCPs need access to credible education, professional support, networking, career development, mentorship, and a regulated testing environment. This will enable them to evaluate the credibility of genetic tests and testing companies, provide genetic results in context, and apply appropriate clinical translation. Key Message: In order to establish an expert group of nutrigenomic practitioners, collaboration is required between educational institutions, professional organizations, and genetic testing companies. This will provide the necessary support, skills, and knowledge to ensure that the best value is extracted from nutrigenetic tests in an ethical and responsible manner.
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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 | 0.070 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.031 | 0.016 |
| Insufficient payload (model declined to judge) | 0.039 | 0.024 |
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".