Evidence-Based Management of Nutrigenomics Expectations and ELSIs
Bibliographic record
Abstract
Nutrigenomics is a new application context for genomics technologies that focuses on the bidirectional study of genetic factors influencing host (individuals' or populations') response to diet and the effects of bioactive constituents in food on host genome and gene expression. Nutrigenomics is considered the next wave after pharmacogenomics for individualization of health interventions. However, relatively little attention has been given to the specific ethical-legal-social issues (ELSIs) and sociotechnical expectations raised by nutrigenomics research. Some of the ELSIs, such as ensuring privacy of genetic information and implications of genetic testing for health insurance and employment, may be shared across the continuum of genomic technology applications in human disease genetics, pharmacogenomics and nutrigenomics. However, there are certain aspects of nutrigenomics research that may result in unique or unprecedented ELSIs. For example, nutrigenomics has a strong focus on public health and the prevention/modification of 'predisease phenotypes' in apparently healthy individuals. Thus, in contrast to previous applications of genomics technologies, where the goal is to distinguish existing disease from absence of disease, the aim of nutrigenomics is the discernment of nuanced differences in predisease states. Moreover, there is evidence to suggest that ELSIs may be different in biomarker discovery, translational research and clinical testing stages of nutrigenomics. Ideally, ELSI research and nutrigenomics bioscience should progress in parallel and in a commensurate manner. We suggest that qualitative research methods, using a hypothesis-free approach, can be employed to gain deeper insights on complex bioethics issues that do not ordinarily lend themselves to formal hypothesis testing with the quantitative methods used in biomedical sciences.
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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 | 0.613 | 0.799 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 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".