Psychological distress following multi‐gene panel testing for hereditary breast and ovarian cancer risk
Bibliographic record
Abstract
Advances in our understanding of the genetic landscape of hereditary breast and ovarian cancer (HBOC) have led to the clinical adoption of multi-gene panel testing. Panel testing introduces new sources of genetic uncertainty secondary to the inclusion of moderate- and low-penetrance genes, as well as the increased likelihood of identifying a variant of uncertain significance (VUS). This cross-sectional study explored the post-test psychological functioning of women who underwent multi-gene panel testing for HBOC susceptibility genes. Two hundred and ninety-five women who underwent panel testing within the previous 2 years completed a study questionnaire to measure levels of cancer-related and genetic testing-related distress using the Impact of Events Scale (IES) and the Multidimensional Impact of Cancer Risk Assessment (MICRA), respectively. Multiple regression analyses were conducted to evaluate the relationship between genetic test results and levels of psychological distress captured by the IES and MICRA. In this cohort, a pathogenic variant (PV) was identified in 41 (14%) of participants, and 77 (26%) participants were found to have a VUS. In the multi-variate model, higher mean levels of genetic testing-related distress were observed in individuals with a PV (p < 0.001) or a VUS (p = 0.007) compared to those with a negative result. Furthermore, participants with a PV in a moderate-penetrance gene were found to have higher levels of genetic testing-related distress compared to those with a PV in a high-risk gene (p = 0.03). Overall, participants were highly satisfied with their genetic testing experience, with 92% of individuals reporting they would recommend testing to others. Our findings highlight differences in psychological outcomes based on both variant pathogenicity and gene penetrance, which contribute to our understanding of the impact of panel testing and sources of both cancer-related and genetic testing-related distress secondary to testing.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".