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Record W4392591630 · doi:10.1016/j.gimo.2024.100958

P076: Implementing tumor-first genetic testing and parent-of-origin-aware genomic analysis into the diagnostic pipeline for hereditary breast cancer

2024· article· en· W4392591630 on OpenAlexaffabout
Haojun Huang, Alexandra Fok, Tracy Tucker, Stephen Yip, Zuzana Kos, Colin Mar, Sophie Sun, Kasmintan A. Schrader

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCanada's Michael Smith Genome Sciences CentreSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsPipeline (software)Genetic testingBreast cancerCancerComputational biologyMedicineOncologyGeneticsBioinformaticsBiologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

In 2023, an estimated 28,400 Canadian women will be diagnosed with breast cancer, and 5,400 will die of it. Genetic mutations in BRCA1, BRCA2, and other genes have been linked to an increased risk of developing this disease. Genetic testing can be used to identify genetic mutations and evaluate the inherited risk factors for preventative purposes. This approach is especially important for high-risk groups, including individuals of Ashkenazi Jewish descent, who are 10 times more likely to carry a BRCA gene mutation.

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 score0.174
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.031
GPT teacher head0.358
Teacher spread0.326 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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