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Record W2125665941 · doi:10.1111/cge.12149

Rates of risk‐reducing surgery in Israeli <i><scp>BRCA1</scp></i> and <i><scp>BRCA2</scp></i> mutation carriers

2013· article· en· W2125665941 on OpenAlexaff
Yael Laitman, Y Vaisman, Dana Feldman, Limor Helpman, M Gitly, Shani Paluch Shimon, Raanan Berger, Liat Cohen, SA Narod, Eitan Friedman

Bibliographic record

VenueClinical Genetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineProphylactic MastectomyMastectomyOophorectomyBreast cancerGenetic testingGynecologyOvarian cancerProphylactic SurgeryBRCA mutationFamily historyMutationOncologyCancerInternal medicineSurgeryHysterectomyGeneticsBiologyGene

Abstract

fetched live from OpenAlex

The frequency of BRCA1 and BRCA2 mutations is higher in Israel than in almost all other countries. One strategy to reduce the burden of hereditary breast and ovarian cancers is to offer genetic testing followed by risk-reducing surgery (mastectomy and salpingo-oophorectomy) for mutation carriers. The extent to which Israeli women who carry mutations undergo these surgeries is not well characterized. Israeli women who are BRCA1 or BRCA2 mutation carriers and followed at a single high-risk clinic were asked to complete a questionnaire detailing their clinical histories at the time of genetic results disclosure and a follow-up questionnaire was completed 18 or more months thereafter. A total of 205 mutation carriers completed the questionnaires. Of 170 women with no cancer history, 84 (49%) had a risk-reducing bilateral salpingo-oophorectomy and 22 (13%) had a risk-reducing mastectomy. Five of 35 (14.3%) women with breast cancer opted for contralateral mastectomy. Approximately one half of Israeli women with a BRCA1 or BRCA2 mutation opt for risk-reducing oophorectomy, but the rate of risk-reducing mastectomy is only 13%.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-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 score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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