Genetic predisposition to male breast cancer in Poland
Bibliographic record
Abstract
BACKGROUND: Breast cancer in men accounts for fewer than 1 % of all breast cancer cases diagnosed in men and women. Genes which predispose to male breast cancer include BRCA1 and BRCA2. The role of other genes is less clear. In Poland, 20 founder mutations in BRCA1, BRCA2, CHEK2, PALB2, NBN, RECQL are responsible for the majority of hereditary breast cancer cases in women, but the utility this genes panel has not been tested in men. METHODS: We estimated the prevalence of 20 alleles in six genes (BRCA1, BRCA2, CHEK2, PALB2, NBN, RECQL) in 165 Polish male breast cancer patients. We compared the frequency of selected variants in male breast cancer cases and controls. RESULTS: One of the 20 mutations was seen in 22 of 165 cases (13.3%). Only one BRCA1 mutation and two BRCA2 mutations were found. We observed statistically significant associations for PALB2 and CHEK2 truncating mutations. A PALB2 mutation was detected in four cases (OR = 11.66; p < 0.001). A CHEK2 truncating mutation was detected in five cases (OR = 2.93;p = 0.02). CONCLUSION: In conclusion, we recommend that a molecular test for BRCA1, BRCA2, PALB2 and CHEK2 recurrent mutations should be offered to male breast cancer patients in Poland.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".