Comparison of guidelines, BRCAPRO, and genetic counsellors estimates for the identification of BRCA1 and BRCA2 mutations in pancreatic cancer.
Bibliographic record
Abstract
e15784 Background: Germline BRCA1 and BRCA2 (BRCA) mutation carriers with pancreatic adenocarcinoma (PDAC) are eligible for precision therapy trials and their relatives should undergo genetic testing and tailored cancer prevention. We assessed the performance of strategies to identify BRCA mutation carriers in PDAC. Methods: Incident cases of PDAC were prospectively recruited for BRCA sequencing in a multidisciplinary PDAC clinic. Probands were evaluated according to the National Comprehensive Cancer Network 2017 (NCCN) and the Ontario Ministry of Health and Long-Term Care (MOHLTC) guidelines for BRCA testing. The probability of each proband carrying a BRCA mutation was estimated using BRCAPRO and by surveying genetic counsellors. Guidelines were compared across sensitivity, specificity, and positive and negative predictive values (PPV and NPV). Estimates from BRCAPRO and the genetic counsellors were compared using the area-under-the-curve (AUC) for discrimination and the Hosmer-Lemeshow test for calibration. Results: 22/484 (4.5%) of probands carried a BRCA mutation. The mutation rate was higher in probands with Ashkenazi Jewish ancestry (7/57, p=0.009) or a first-degree relative with breast cancer (8/83, p=0.036). 119 genetic counsellors responded to the survey and each proband was assessed by a mean of 5.9 genetic counsellors. The Table displays the performance of the guidelines. Discrimination was similar for the estimates from genetic counsellors and BRCAPRO (AUC 0.755 and 0.775, respectively, p=0.701). Genetic counsellors generally overestimated (p=0.008), whereas BRCAPRO severely underestimated (p<0.001), the probability that each proband carried a mutation. Conclusions: The NCCN 2017 guidelines and estimates from genetic counsellors accurately identify BRCA mutations in PDAC. The MOHLTC guidelines and BRCAPRO should be updated to account for the association between PDAC and BRCA mutations. [Table: see text]
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".