Retrospective Study of Genetic Testing Results Reveals Pathogenic Variants Beyond <scp>BRCA1</scp>/2 in Hereditary Breast and Ovarian Cancer Cases in New Brunswick: Implications for Future Care
Bibliographic record
Abstract
BACKGROUND: In Canada, founder variants in breast cancer susceptibility genes have been identified in populations residing in Québec and Newfoundland, thus demonstrating the value in characterizing the genetic profile of local populations for better clinical management. New Brunswick has a diverse, yet genetically unexplored population that includes founder Irish and Acadian ancestry, among others, and we hypothesized that this population could demonstrate potential enrichments for variants in breast cancer genes. METHODS: Health records were retrospectively analyzed for 445 cases referred to the genetics clinic in Moncton, New Brunswick, their molecular results were summarized and compared to allele frequencies from similar studies in Canada. RESULTS: No ethnic or age-related correlation for specific variants could be identified. However, BRCA1/2 variant frequency was lower than expected in the study group and variants in other susceptibility genes such as ATM and CHEK2 were higher when compared to similar studies. PERSPECTIVES: This study demonstrates a distinct profile in hereditary breast cancer genetics in a previously uncharacterized population, thus adding to existing knowledge of population genetics in Atlantic Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".