A randomized trial of genetic information for personalized nutrition on behaviour outcomes
Bibliographic record
Abstract
Personal genetic information has become increasingly accessible to the public as a result of direct‐to‐consumer genetic tests, however, concerns have been raised over their value and potential risks. We compared the effects of providing genotype‐based dietary advice with general recommendations on behaviour outcomes using a randomized controlled study. Subjects aged 20–35 years (n=138) were randomized to an intervention (I) or control (C) group and were given a report of either genotype‐based or general dietary advice, respectively. A survey was completed to assess understanding and opinions of the reports. Responses were given on a 5‐point scale ranging from “strongly agree” to “strongly disagree”. Subjects reporting “strongly agree” or “somewhat agree” were grouped and the chi‐square test was used to compare frequency of “agree” to all other responses. Subjects in the intervention group were more likely to agree that they understood the report (93% (I) vs. 78% (C); p=0.009), that the advice would be useful when considering diet (88% (I) vs. 72% (C); p=0.02) and that they would like to know more about the dietary advice (95% (I) vs. 76% (C); p=0.001). Only 9% of subjects in the intervention group reported feeling uneasy about learning their genetic information. These findings suggest that individuals find dietary recommendations based on genetics more understandable and more useful than general dietary advice. Grant Funding Source : Advanced Foods and Materials Network
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".