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Record W2139557430 · doi:10.1136/jmg.2004.024133

Breast and ovarian cancer risks to carriers of the BRCA1 5382insC and 185delAG and BRCA2 6174delT mutations: a combined analysis of 22 population based studies

2005· letter· en· W2139557430 on OpenAlexaff
Antonis C. Antoniou, Paul D.P. Pharoah, Steven A. Narod, Harvey A. Risch, Jórunn E. Eyfjörd, John L. Hopper, Håkan Olsson, Oskar T. Johannsson, Åke Borg, Barbara Pasini, Paolo Radice, Siranoush Manoukian, Diana Eccles, Nelson L.S. Tang, Edith Olah, Hoda Anton‐Culver, Ellen Warner, Jan Lubiński, Jacek Gronwald, Bartłomiej Górski, H Tulinius, Steinunn Thorlacius, Hannaleena Eerola, Heli Nevanlinna, Kirsi Syrjäkoski, Deborah Thompson, Chris Evans, Julian Peto, Fiona Lalloo, D. Gareth Evans, Douglas F. Easton

Bibliographic record

VenueJournal of Medical Genetics · 2005
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteCancer Research UKNational Institutes of HealthHungarian Scientific Research FundHáskóli ÍslandsCancerfonden
KeywordsOvarian cancerBreast cancerPopulationFounder effectMutationOncologyMeta-analysisGeneticsCancerMedicineBiologyInternal medicineGynecologyGeneGenotypeHaplotypeEnvironmental health

Abstract

fetched live from OpenAlex

A recent report estimated the breast cancer risks in carriers of the three Ashkenazi founder mutations to be higher than previously published estimates derived from population based studies. In an attempt to confirm this, the breast and ovarian cancer risks associated with the three Ashkenazi founder mutations were estimated using families included in a previous meta-analysis of populatrion based studies. The estimated breast cancer risks for each of the founder BRCA1 and BRCA2 mutations were similar to the corresponding estimates based on all BRCA1 or BRCA2 mutations in the meta-analysis. These estimates appear to be consistent with the observed prevalence of the mutations in the Ashkenazi Jewish population.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.638

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 integrity0.0010.000
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 head0.025
GPT teacher head0.340
Teacher spread0.315 · 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

Citations141
Published2005
Admission routes1
Has abstractyes

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