BRCA Testing for Patients Treated in Italy: A National Survey of Breast Centers Associated with Senonetwork
Bibliographic record
Abstract
BACKGROUND: Breast units (BUs) provide breast cancer (BC) care, including prevention, treatment, and genetic assessment. Genetic research has highlighted BRCA1/2 mutations as key hereditary BC risk factors. BRCA testing is crucial for personalized treatment and prevention strategies. However, the integration of BRCA testing in Italian BUs faces multiple challenges. This study, by Senonetwork Italia, aimed to evaluate genetic testing practices and identify obstacles within Italian BUs. METHODS: Senonetwork Italia conducted a 16-question web-based survey involving 153 BUs. The survey assessed aspects of BRCA testing, including timing, urgency, counseling, patient selection, and multi-gene panels. RESULTS: Of the 153 BUs, 109 (71.2%) responded. Testing before surgery was performed by 70.6% of centers, with urgent cases acknowledged by 87.2%. Most centers (56.0%) arranged urgent pre-test counseling within a week. BRCA mutation status influenced treatment decisions in 99.1% of cases. Multi-gene panels were used by 33.0% of centers for all genetic counseling cases, while 56.0% followed standard referral criteria. The main challenges included cost, reimbursement, and reporting timelines. CONCLUSIONS: This survey highlights significant variations in BRCA testing practices across Italian BUs and identifies key logistical and financial challenges. There is a need for standardized practices of genetic testing to ensure personalized and effective BC management in Italy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".