Genetics professionals' perspectives on reporting incidental findings from clinical genome‐wide sequencing
Bibliographic record
Abstract
Whole exome or whole genome analysis using massively parallel sequencing technologies will undoubtedly solve diagnostic dilemmas; however, incidental findings (IF) that may have medical and social implications will also be discovered. While there is consensus in the literature that analytically valid and medically actionable IF should be returned to patients if requested, there is debate regarding the return of other IF. There are currently no guidelines established for managing IF in the clinical context. We therefore distributed an online questionnaire to 496 geneticists and genetic counselors in Canada to explore this unresolved issue, and 210 professionals participated (response rate = 42%). The proportion of respondents who indicated that they would return IF to patients depended on the nature of the finding, ranging from 95% for information pertaining to a serious and treatable condition to 12% for information with only social implications (e.g., non-paternity). There was a lack of consensus around the disclosure of certain IF such as genetic carrier status, especially for pediatric patients. The most important considerations identified as impacting IF disclosure included condition-specific factors such as treatment availability, test accuracy, and evidence indicating pathogenicity. This is the first study to document the views of geneticists and genetic counselors in Canada towards the disclosure of IF, and represents a step towards evidence-based guidelines for clinical genome-wide sequencing investigations.
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.
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 | 0.101 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| 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".