An Update to Returning Genetic Research Results to Individuals: Perspectives of the Industry Pharmacogenomics Working Group
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.
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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 | 0.092 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.057 | 0.071 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".