Pharmacogenomics: Reflecting on the Old and New Social, Ethical and Policy Issues in Postgenomics Medicine
Bibliographic record
Abstract
Whenever a new form of biotechnology is introduced, there is often uncertainty around its intended and unintended impacts on science, medicine and society. Past experiences with genetically modified organisms, stem cell research and other health technologies have taught us some important lessons – that it is not just scientific and technical factors that are important to the uptake of innovative technologies. In the case of pharmacogenomics, a field of inquiry that aims to discern the genomic basis of individual and population differences in drug effects, there has been much written on the attendant promises and limitations. Since the completion of the Human Genome Project in 2003, we are, however, in the postgenomics era. This brings some of the ‘old questions’ that remained unaddressed in pharmacogenomics to the forefront, e.g. race-based therapeutics. Moreover, ‘new questions’ in postgenomics medicine – such as privacy and confidentiality in hypothesis-free genome-wide association studies, and regulation of direct-to-consumer personal genomics tests – require critical reexamination of the established practices in both biosciences and bioethics. While other chapters in this book aim to address the technical and scientific factors, the present chapter presents an analyses of the old and new social, ethical and policy issues that can impact the uptake of pharmacogenomic innovations and their future trajectory in postgenomics medicine.
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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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.023 | 0.039 |
| 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; 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".