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Comparison of guidelines, BRCAPRO, and genetic counsellors estimates for the identification of BRCA1 and BRCA2 mutations in pancreatic cancer.

2017· article· en· W2890226048 on OpenAlexaffabout
Robert C. Grant, Spring Holter, Ayelet Borgida, Melania Pintile, Mohammad R. Akbari, George Zogopoulos, Steven Gallinger

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill University Health CentrePrincess Margaret Cancer CentreMount Sinai HospitalWomen's College HospitalToronto General HospitalUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineProbandGenetic counselingGenetic testingBRCA mutationOncologyInternal medicineCancerPancreatic cancerMutationBreast cancerGeneticsBiologyGene

Abstract

fetched live from OpenAlex

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]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.700
GPT teacher head0.712
Teacher spread0.012 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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