An Update to Returning Genetic Research Results to Individuals: Perspectives of the Industry Pharmacogenomics Working Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ease with which genotyping technologies generate tremendous amounts of data on research participants has been well chronicled, a feat that continues to become both faster and cheaper to perform. In parallel to these advances come additional ethical considerations and debates, one of which centers on providing individual research results and incidental findings back to research participants taking part in genetic research efforts. In 2006 the Industry Pharmacogenomics Working Group (I-PWG) offered some 'Points-to-Consider' on this topic within the context of the drug development process from those who are affiliated to pharmaceutical companies. Today many of these points remain applicable to the discussion but will be expanded upon in this updated viewpoint from the I-PWG. The exploratory nature of pharmacogenomic work in the pharmaceutical industry is discussed to provide context for why these results typically are not best suited for return. Operational challenges unique to this industry which cause barriers to returning this information are also explained.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it