Family Information Service Participation Increases the Rates of Mutation Testing Among Members of Families with<i>BRCA1/2</i>Mutations
Bibliographic record
Abstract
Some members of hereditary breast-ovarian cancer (HBOC) families may not participate in BRCA testing to determine their mutation status in part because they are unaware of their cancer risk and the availability of BRCA testing. Participation in a family information service (FIS), of which we have provided more than 100 sessions during the past 30 years, has been seen to effectively allow family members to be educated regarding their cancer genetic risk and potential benefits from cancer control measures such as mutation testing. However, the effect of the FIS on the rate of mutation testing has not been studied. One thousand five hundred seventy-four eligible (>18-year old, at a 25% or higher pedigree risk) members from 60 extended HBOC families with BRCA1/2 mutations were invited to attend a FIS to learn about their risk and undergo genetic testing. The rates of mutation testing were compared between those who had attended an FIS, and those who had not with chi-squared test and logistic regression analysis. Seventy five percent (334/444) of FIS attendees had undergone mutation testing following or during an FIS which was significantly higher than the 33.8% (382/1130) rate among nonattendees (p < 0.0001). Logistic regression analysis showed that FIS attendance, breast-ovarian cancer history, gender, and age were significant variables for undertaking a mutation test. FIS attendance significantly increased the rate of mutation testing among high-risk family members.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".