Clinical practice guidelines for BRCA1 and BRCA2 genetic testing
Bibliographic record
Abstract
BRCA1 and BRCA2 gene pathogenic variants account for most hereditary breast cancer and are increasingly used to determine eligibility for PARP inhibitor (PARPi) therapy of BRCA-related cancer. Because issues of BRCA testing in clinical practice now overlap with both preventive and therapeutic management, updated and comprehensive practice guidelines for BRCA genotyping are needed. The integrative recommendations for BRCA testing presented here aim to (1) identify individuals who may benefit from genetic counselling and risk-reducing strategies; (2) update germline and tumour-testing indications for PARPi-approved therapies; (3) provide testing recommendations for personalised management of early and metastatic breast cancer; and (4) address the issues of rapid process and tumour analysis. An international group of experts, including geneticists, medical and surgical oncologists, pathologists, ethicists and patient representatives, was commissioned by the French Society of Predictive and Personalised Medicine (SFMPP). The group followed a methodology based on specific formal guidelines development, including (1) evaluating the likelihood of BRCAm from a combined systematic review of the literature, risk assessment models and expert quotations, and (2) therapeutic values of BRCAm status for PARPi therapy in BRCA-related cancer and for management of early and advanced breast cancer. These international guidelines may help clinicians comprehensively update and standardise BRCA testing practices.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".