Ethical, Legal, and Regulatory Issues for the Implementation of Omics-Based Risk Prediction of Women’s Cancer: Points to Consider
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Advances in omics open new opportunities for cancer risk prediction and risk-based screening interventions. However, implementation of risk prediction in clinical practice may impact the ethical, legal, and regulatory aspects of current cancer screening programs. In order to support decision-making, we analyzed the ethical, legal, and regulatory issues and developed a set of Points to Consider to support management of these issues. METHODS: We analyzed the legal and policy frameworks applicable to breast and cervical cancer screening programs in 7 European countries. We identified the most relevant issues to be considered, and we developed considerations for their management, based on the literature, the legal and policy frameworks, and our experience with similar issues. RESULTS: The considerations focus on five topics: (A) health services planning, (B) information and invitation, (C) consent and data/sample collection, (D) risk calculation and communication of results, and (E) storage of data and residual samples. CONCLUSION: Current frameworks might not be adequate to implement a risk prediction approach using omics factors due to the different characteristics of such approaches.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".