Genetic risk assessment and prevention: the role of genetic testing panels in breast cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multigene panel tests are being increasingly used for the genetic assessment of women with an apparent predisposition to breast cancer. Here, we review all studies reporting results from individuals who have undergone multigene panel testing for hereditary breast cancer. Across all gene panel studies, the prevalence of pathogenic mutations was highest in BRCA1 (5.3%) and BRCA2 (3.6%) and was lowest in PTEN (0.1%), CDH1 (0.1%) and STK11 (0.01%). After BRCA1/2, the prevalence of pathogenic mutations was highest in CHEK2 (1.3%), PALB2 (0.9%) and ATM (0.8%). The prevalence of variants of unknown significance was highest in ATM (9.6%). Based on the prevalence and penetrance of pathogenic mutations and the prevalence of variants of unknown significance, it is our interpretation that BRCA1, BRCA2, PALB2 and CHEK2 are the best candidates for inclusion in a clinical multigene breast cancer panel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it