Information Provided to Consumers about Direct-to-Consumer Nutrigenetic Testing
Bibliographic record
Abstract
BACKGROUND: Nutrigenetic tests are often considered to be less serious compared to other health-related genetic tests, although they share similar ethical concerns. Nutrigenetic tests are mainly available through direct-to-consumer genetic testing (DTC GT) and increasing in popularity. OBJECTIVE: To analyze the contents of nutrigenetic DTC GT websites with respect to the adequacy of the information provided to support a well-informed decision of purchasing the tests. METHODS: The websites of DTC GT companies selling nutrigenetic tests that could be ordered online without involving any healthcare professional, available in English, marketing tests in Europe, the USA, Australia, or Canada, and accessible from Finland were included in the study (n = 38). Quantitative and qualitative content analyses of the websites were carried out with the help of a codebook. RESULTS: Of the 38 websites, 8 included a clearly identifiable and easy-to-find information section about genetics. The quality and contents of these sections were often insufficient and/or misleading. Fourteen websites had specific sections discussing the risks related to GT, and on 13 signed informed consent was requested for GT. Furthermore, only 2 of the companies offered any kind of pretest consultation and 13 offered mostly separately charged posttest consultation. The complex structure of the websites made it difficult to find all key information, with many important aspects buried in legal documents, which were challenging to comprehend even for a professional. CONCLUSION: The structure of the websites and the amount and quality of the content therein do not support a well-informed decision.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".