Implications of the Age Range in a Population-based <i>BRCA1</i> Testing Program with Eligibility Based on Family History of Breast and Ovarian Cancer
Bibliographic record
Abstract
The current options available to BRCA1 mutation carriers can be classified as either cancer risk reduction or increased disease surveillance. Risk reduction might be preferable to young women. Increased surveillance might be more attractive to women when their cancer risk is highest. The aim of this report is to estimate the sensitivity, specificity and ability to detect carriers for a population-based BRCA1 testing program with eligibility based on family history of cancer, and examine the effect of age on the program's performance. A computer model was used to simulate the incidence of breast and ovarian cancer in a woman's family, based on her BRCA1 mutation carrier status. Age-specific estimates of the sensitivity and specificity for family history as an indicator of mutation status were applied to local population figures. Sensitivity of the program increased with the age of the proband and the size of her family. Sensitivity ranged from 0.33 for 20-year-olds with small families, to 0.98 for 60-year-olds with large families. Specificity was greater than 0.95, regardless of a woman's age or family size. If 0.12% of people carry a BRCA1 mutation, a province-wide testing program for people aged 20-69 with referrals based only on family history would have a sensitivity of 0.55. Only 2% of the genetic test results would be positive. The acceptability of a genetic testing program depends on its sensitivity and specificity, and on the options available to women who are found to carry a mutation. Compared with variation due to family size, the program sensitivity and specificity does not differ substantially amongst the various age groups.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".