Inclusion and exclusion in nutrigenetics clinical research: ethical & scientific challenges
Bibliographic record
Abstract
Background/Aims: There are compelling reasons to ensure 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: 173 publications were identified through a systematic review of clinical studies in nutrigenetics published between 1998 and 2007 inclusively. 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.
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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 | 0.609 | 0.700 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".