Psychological Impact of Genetic Testing for Breast Cancer Susceptibility in Women of Ashkenazi Jewish Background: A Prospective Study
Bibliographic record
Abstract
The recognition that the prevalence of three founder mutations in the BRCA1 and BRCA2 genes is over 2% in Ashkenazi Jews has resulted in numerous epidemiological research studies of this ethno-religious group. To determine the effects of incorporating research into clinical practice, a psychological impact study of women participating in an epidemiological study was conducted. Sixty women of Ashkenazi Jewish background who underwent genetic testing for founder mutations were assessed using mailed, self-administered questionnaires with validated measures of psychological outcome. Forty-three women elected to learn their results and 17 women declined to do so. Women who elected to learn their results were also assessed 7-10 days, 4 months, and 12 months after results disclosure. Women who chose to learn their results had significantly higher baseline breast cancer anxiety, compared to those who elected not to learn their results (z = -2.27; p = 0.023). Unaffected women who elected to learn their results showed a significant decrease in breast cancer anxiety 4 months (z = -2.37, p = 0.018) and 12 months (z = -3.06, p = 0.002) post-notification compared to baseline. Genetic testing for mutations common in Ashkenazi Jewish women with result disclosure does not lead to adverse psychological outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".