Patient satisfaction and cancer‐related distress among unselected Jewish women undergoing genetic testing for BRCA1 and BRCA2
Bibliographic record
Abstract
It is not known to what extent participation in a genetic testing program for BRCA1 and BRCA2, which does not include an extensive pre-test counselling session, influences cancer-related distress, cancer risk perception and patient satisfaction. Unselected Jewish women in Ontario were offered genetic testing for three common Jewish BRCA mutations. Before testing and 1-year post-testing, the women completed questionnaires which assessed cancer-related distress, cancer risk perception, and satisfaction. A total of 2080 women enrolled in the study; of these, 1516 (73%) completed a 1-year follow-up questionnaire. In women with a BRCA mutation, the mean breast cancer risk perception increased from 41.1% to 59.6% after receiving a positive genetic test result (p = 0.002). Among non-carriers, breast cancer risk perception decreased slightly, from 35.8% to 33.5% (p = 0.08). The mean level of cancer-related distress increased significantly for women with a BRCA mutation, but did not change in women without a mutation; 92.8% expressed satisfaction with the testing process. The results of this study suggest that the majority of Jewish women who took part in population genetic screening for BRCA1 and BRCA2 were satisfied with the delivery of genetic testing and would recommend testing to other Jewish women. However, women with a BRCA mutation experienced increased levels of cancer-related distress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".