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Record W4413871486 · doi:10.1186/s13053-025-00321-y

Medullary breast cancer and germline BRCA1 mutations: a possible criterion for genetic testing

2025· review· en· W4413871486 on OpenAlexaff
Adriana Ionelia Apostol, David W. Lim, Steven A. Narod

Bibliographic record

VenueHereditary Cancer in Clinical Practice · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioWomen's College Hospital
Fundersnot available
KeywordsBreast cancerMedicineMedullary cavityOncologyGenetic testingCancerGermline mutationMedullary carcinomaInternal medicineMutationGenetic counselingMale breast cancerGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Medullary breast cancer is a rare subtype of invasive breast cancer, representing from 0.2% to 6% of all breast carcinomas, with a higher proportion among women with triple-negative breast cancer and among those with a BRCA1 mutation. This review article aims to investigate the frequency of medullary breast cancer among all breast cancers and to assess its association with BRCA1 mutations. We surveyed studies involving patients diagnosed with breast cancer that report both the histology of the breast cancer as well as the presence of BRCA1 mutations. Among women with medullary breast cancer, the proportion of cases that carry a BRCA1 mutation ranges from 3% up to 35.3%, depending on the study. Among BRCA1-mutated breast cancers, the proportion that are medullary ranges from 8 to 20%. Given the notable association between medullary breast cancer and BRCA1 mutations, we propose to consider medullary breast cancer as a criterion for genetic testing in order to improve the identification of a larger number of carriers, thereby enhancing screening and prevention strategies.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.088
GPT teacher head0.474
Teacher spread0.386 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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