Inclusion and Exclusion in Nutrigenetics Clinical Research: Ethical and Scientific Challenges
Bibliographic record
Abstract
BACKGROUND/AIMS: There are compelling reasons to ensure the participation of ethnic minorities and populations of all ages worldwide in nutrigenetics clinical research. If findings in such research are valid for some individuals, groups, or communities, and not for others, then ethical questions of justice--and not only issues of methodology and external validity--arise. This paper aims to examine inclusion in nutrigenetics clinical research and its scientific and ethical challenges. METHODS: In total, 173 publications were identified through a systematic review of clinical studies in nutrigenetics published between 1998 and 2007. Data such as participants' demographics as well as eligibility criteria were extracted. RESULTS: There is no consistency in the way participants' origins (ancestry, ethnicity, or race) and ages are described in publications. A vast majority of the studies identified was conducted in North America and Europe and focused on 'white' participants. Our results show that pregnant women (and fetuses), minors, and the elderly (≥ 75 years old) remain underrepresented. CONCLUSION: Representativeness in nutrigenetics research is a challenging ethical and scientific issue. Yet, if nutrigenetics is to benefit whole populations and be used in public and global health agendas, fair representation as well as clear descriptions of participants in publications are crucial.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".