A comparison of the detection of BRCA mutation carriers through the provision of Jewish population-based genetic testing compared with clinic-based genetic testing
Bibliographic record
Abstract
BACKGROUND: Guidelines for genetic testing for BRCA1 or BRCA2 stipulate that a personal or family history of cancer is necessary to be eligible for testing. Approximately 2% of Ashkenazi Jewish women carry a mutation, but to date population-based testing has not been advocated. Little is known about the relative yield of a conventional genetic testing programme versus a programme of widespread testing in a population with common founder mutations. METHODS: We provided both referral-based and Jewish population-based testing between 2008 and 2012. We compared the numbers of BRCA mutation carriers identified through the two streams and estimated the number of genetic counselling hours devoted to each programme. RESULTS: From 2008 to 2012, 38 female carriers were identified through 487 referrals to our genetics centre (29 unaffected with cancer). During the same time, 6179 Jewish women were tested through our population-based programme and 93 mutation carriers were identified (92 unaffected with cancer). Fewer counsellor hours were devoted to the population-based than to the clinical referral-based testing programme. CONCLUSION: Genetic testing of all Jewish women above the age of 25 years will greatly expand the number of BRCA mutation carriers identified without a commensurate increase in the number of hours required for counselling.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".