Comparison of Practice Guidelines, BRCAPRO, and Genetic Counselor Estimates to Identify Germline <i>BRCA1</i> and <i>BRCA2</i> Mutations in Pancreatic Cancer
Bibliographic record
Abstract
Germline BRCA1 and BRCA2 (BRCA) mutation carriers with pancreatic ductal adenocarcinoma (PDAC) may benefit from precision therapies and their relatives should undergo tailored cancer prevention. In this study, we compared strategies to identify BRCA carriers with PDAC. Incident cases of PDAC were prospectively recruited for BRCA sequencing. Probands were evaluated using the National Comprehensive Cancer Network (NCCN) and the Ontario Ministry of Health and Long-Term Care (MOHLTC) guidelines. The probability of each proband carrying a mutation was estimated by surveying genetic counselors and using BRCAPRO. BRCA mutations were detected in 22/484 (4.5%) probands. 152/484 (31.2%) and 16/484 (3.3%) probands met the NCCN and MOHLTC guidelines, respectively. The NCCN guidelines had higher sensitivity than the MOHLTC guidelines (0.864 versus 0.227, P < 0.001) but lower specificity (0.712 versus 0.976, P < 0.001). One hundred and nineteen genetic counselors completed the survey. Discrimination was similar between genetic counselors and BRCAPRO (area-under-the-curve: 0.755 and 0.775, respectively, P = 0.702). Genetic counselors generally overestimated (P = 0.008), whereas BRCAPRO severely underestimated (P < 0.001), the probability that each proband carried a mutation. Our results indicate that the NCCN guidelines and genetic counselors accurately identify BRCA mutations in PDAC, while the MOHLTC guidelines and BRCAPRO should be updated to account for the association between BRCA and PDAC.
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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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".