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Record W2990927974 · doi:10.1159/000503977

Information Provided to Consumers about Direct-to-Consumer Nutrigenetic Testing

2019· article· en· W2990927974 on OpenAlexaboutno aff
Suchetana De, Anna‐Maija Pietilä, Terhi Iso‐Touru, Anu Hopia, Raija Tahvonen, Kirsi Vähäkangas

Bibliographic record

VenuePublic Health Genomics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityQuality (philosophy)PsychologyMedicineMarketingBusinessSocial psychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.266
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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