MétaCan
Menu
Back to cohort
Record W2254155967 · doi:10.1136/jmedgenet-2015-103132

Comprehensive spectrum of <i>BRCA1</i> and <i>BRCA2</i> deleterious mutations in breast cancer in Asian countries

2015· review· en· W2254155967 on OpenAlexafffundabout
Ava Kwong, Vivian Y. Shin, John Chi Wang Ho, Eunyoung Kang, Seigo Nakamura, Soo‐Hwang Teo, Ann S. G. Lee, Jen-Hwei Sng, Ophira Ginsburg, Allison W. Kurian, Jeffrey N. Weitzel, Man-Ting Siu, F. B. F. Law, Tsun‐Leung Chan, Steven A. Narod, James M. Ford, Edmond S.K., Sung‐Won Kim

Bibliographic record

VenueJournal of Medical Genetics · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsWomen's College HospitalUniversity of Toronto
FundersNIH Office of the DirectorNational Cancer InstituteNational Institutes of HealthKerry Group Kuok FoundationCanadian Cancer Society Research InstituteCanadian Breast Cancer Research Alliance
KeywordsBreast cancerGermline mutationEthnic groupMainland ChinaChinaCancerGeneticsMutationGermlineMedicineGeneBiologyGeographyPolitical science

Abstract

fetched live from OpenAlex

Approximately 5%-10% of breast cancers are due to genetic predisposition caused by germline mutations; the most commonly tested genes are BRCA1 and BRCA2 mutations. Some mutations are unique to one family and others are recurrent; the spectrum of BRCA1/BRCA2 mutations varies depending on the geographical origins, populations or ethnic groups. In this review, we compiled data from 11 participating Asian countries (Bangladesh, Mainland China, Hong Kong SAR, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Thailand and Vietnam), and from ethnic Asians residing in Canada and the USA. We have additionally conducted a literature review to include other Asian countries mainly in Central and Western Asia. We present the current pathogenic mutation spectrum of BRCA1/BRCA2 genes in patients with breast cancer in various Asian populations. Understanding BRCA1/BRCA2 mutations in Asians will help provide better risk assessment and clinical management of breast cancer.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.348
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations111
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Medical GeneticsSame topicBRCA gene mutations in cancerFrench-language works237,207