Global Perspectives on Managing Incidental and Secondary Findings in Genomic Testing: A Comprehensive Review of Policies, Implementation Challenges, and Stakeholder Perspectives
Bibliographic record
Abstract
The rapid advancement of genome sequencing has increased the detection of incidental findings (IFs) and secondary findings (SFs), raising complex ethical and practical challenges in both clinical and research settings. This review examines policies, guidelines, and stakeholder perspectives on IF/SF across different jurisdictions, focusing on articles published between 2000 and 2024. We found significant variation in IF/SF reporting practices, reflecting different healthcare systems and ethical frameworks. While the American College of Medical Genetics and Genomics supports proactive SF reporting, European and Canadian policies adopt more conservative approaches. Stakeholder perspectives also varied; patients generally preferred receiving results, whereas healthcare professionals' support depended on factors including actionability and patient age. Particular challenges emerged in relation to pediatric cases, with ongoing debates about balancing future autonomy with potential medical benefits. Implementation barriers were identified across jurisdictions, including resource constraints, knowledge limitations, and a lack of standardized procedures. Despite consensus on the potential value of IF/SF reporting, inconsistencies in approaches and implementation challenges persist. Current evidence suggests the need for more sophisticated, context-sensitive frameworks that can accommodate different healthcare systems while maintaining consistent ethical standards. Further research is required to understand the long-term effects of different reporting approaches on patients, healthcare systems, and society.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".