MétaCan
Menu
Back to cohort
Record W1545950342 · doi:10.1111/cge.12216

Clinical implications of genetic testing for <scp>BRCA1</scp> and <scp>BRCA2</scp> mutations in Austria

2013· article· en· W1545950342 on OpenAlexaff
Christian F. Singer, Daniela Muhr, Christine Rappaport, M. Tea, Daphne Gschwantler‐Kaulich, A. Fink-Retter, G Pfeiler, Andreas Berger, Peng Sun, SA Narod

Bibliographic record

VenueClinical Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineLumpectomyTamoxifenBreast cancerOophorectomyMastectomyProphylactic MastectomyGenetic testingMammographyGynecologyOncologyCancerInternal medicineObstetricsHysterectomySurgery

Abstract

fetched live from OpenAlex

The objective of this study was to describe the experience of genetic testing in Austrian women with a BRCA1 or BRCA2 mutation in terms of preventive measures taken and incident cancers diagnosed. We collected clinical information on 246 Austrian women with a BRCA1 or BRCA2 mutation tested between 1995 and 2012 and followed 182 of them for an average of 6.5 years. Of the 90 women who were cancer-free at baseline, 21.4% underwent preventive bilateral mastectomy, 46.1% had preventive bilateral salpingo-oophorectomy, and 1 took tamoxifen; 58.8% of the at-risk women underwent at least one screening breast magnetic resonance imaging (MRI). Of the 85 women with breast cancer, 69.4% had a unilateral mastectomy or lumpectomy and 30.6% had a contralateral mastectomy. In the follow-up period, 14 new invasive breast cancers (6 first primary and 8 contralateral), 1 ductal carcinoma in situ case, 2 incident ovarian cancer cases, and 1 peritoneal cancer were diagnosed. In Austria, the majority of healthy women with a BRCA1 or BRCA2 mutation opt for preventive oophorectomy and MRI screening to manage their breast cancer risk; few have preventive mastectomy or take tamoxifen.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
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 science0.0000.000
Research integrity0.0010.000
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 head0.078
GPT teacher head0.388
Teacher spread0.310 · 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.

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

Citations7
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueClinical GeneticsSame topicBRCA gene mutations in cancerFrench-language works237,207