Qualitative thematic analysis of consent forms used in cancer genome sequencing
Bibliographic record
Abstract
BACKGROUND: Large-scale whole genome sequencing (WGS) studies promise to revolutionize cancer research by identifying targets for therapy and by discovering molecular biomarkers to aid early diagnosis, to better determine prognosis and to improve treatment response prediction. Such projects raise a number of ethical, legal, and social (ELS) issues that should be considered. In this study, we set out to discover how these issues are being handled across different jurisdictions. METHODS: We examined informed consent (IC) forms from 30 cancer genome sequencing studies to assess (1) stated purpose of sample collection, (2) scope of consent requested, (3) data sharing protocols (4) privacy protection measures, (5) described risks of participation, (6) subject re-contacting, and (7) protocol for withdrawal. RESULTS: There is a high degree of similarity in how cancer researchers engaged in WGS are protecting participant privacy. We observed a strong trend towards both using samples for additional, unspecified research and sharing data with other investigators. IC forms were varied in terms of how they discussed re-contacting participants, returning results and facilitating participant withdrawal. Contrary to expectation, there were no consistent trends that emerged over the eight year period from which forms were collected. CONCLUSION: Examining IC forms from WGS studies elucidates how investigators are handling ELS challenges posed by this research. This information is important for ensuring that while the public benefits of research are maximized, the rights of participants are also being appropriately respected.
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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 | 0.221 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".