The NCCN Criterion “Young Age at Onset” Alone is Not an Indicator of Hereditary Breast Cancer in Iranian Population
Bibliographic record
Abstract
Abstract Because the contribution of genetic factors to the burden of breast cancer is not well investigated in Iran, we aimed to examine the prevalence of mutations in breast cancer susceptibility genes, BRCA1/2 and PALB2, and to investigate the predictive potential of hereditary breast cancer risk criteria for genetic testing in Iranian population. Next-generation sequencing was conducted on a population consisting of 299 and 125 patients with breast cancer, with and without hereditary cancer risk criteria for genetic testing, respectively. The pathogenic mutation frequency rate was 10.7% in patients with hereditary cancer criteria versus 1.6% in no criteria group (P = 0.0017). None of the 107 tested patients with only young age at onset (<40) criterion had a pathogenic mutation. Patients who had only a single heritable risk criterion [OR, 6.15; 95% confidence interval (CI), 1.26–58.59; P = 0.009] and patients with multiple heritable risk criteria (OR, 22.5; 95% CI, 5.19–201.31; P < 0.0001) had higher probabilities of carrying a mutation compared with no criteria group. Our results showed that young age at onset alone is not an indicator of hereditary breast cancer at least in the Iranian population. This is while women with multiple hereditary breast cancer risk criteria were enriched for BRCA1/2 mutations. Given such high risk of identification of a disease-causing mutation, multiple hereditary criteria should be regarded as a strong predictor for a hereditary breast cancer syndrome. These findings are important concerning the optimization of genetic counseling and furthermore establishing criteria for BRCA1/2 testing of the Iranian population.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".