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Record W2016500332 · doi:10.1111/cge.12602

Prevalence of founder mutations in the <scp>BRCA1</scp> and <scp>BRCA2</scp> genes among unaffected women from the Bahamas

2015· article· en· W2016500332 on OpenAlexaff
Magan Trottier, John Lunn, Raleigh Butler, D. Curling, Theodore Turnquest, William Francis, Darron Halliday, Robert E. Royer, S. Zhang, S. Li, Islay Thompson, Talia Donenberg, Judith Hurley, Mohammad R. Akbari, Steven A. Narod

Bibliographic record

VenueClinical Genetics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsFamily historyOvarian cancerBreast cancerFounder effectBRCA mutationGenetic testingGeneticsGenetic counselingPopulationMedicineCancerMutationOncologyBiologyGeneGynecologyInternal medicineHaplotypeGenotype

Abstract

fetched live from OpenAlex

Population-based testing for BRCA1/2 mutations detects a high proportion of carriers not identified by cancer family history-based testing. We sought to determine whether population-based testing is an effective approach to genetic testing in the Bahamas, where 23% of women with breast cancer carry one of seven founder mutations in the BRCA1 or BRCA2 gene. We determined the prevalence of founder BRCA mutations in 1847 Bahamian women without a personal history of breast or ovarian cancer, unselected for age or family history. We found that 2.8% (20/705) of unaffected women with a family history of breast/ovarian cancer and 0.09% (1/1089) of unaffected women without a family history carry a BRCA mutation. A total of 38% of unaffected women with a known mutation in the family were found to carry the familial mutation. We previously suggested that all Bahamian women with breast or ovarian cancer be offered genetic testing. These current data suggest that additionally all unaffected Bahamian women with a family history of breast/ovarian cancer should be offered genetic testing for the founder BRCA mutations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.334
Teacher spread0.287 · 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 teacher head, 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".

Quick stats

Citations31
Published2015
Admission routes1
Has abstractyes

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