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Record W4220984610 · doi:10.3390/curroncol29030172

Risk-Reducing Options for High-Grade Serous Gynecologic Malignancy in BRCA1/2

2022· review· en· W4220984610 on OpenAlexaffvenue
Lauren Clarfield, Laura Diamond, Michelle Jacobson

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsMount Sinai HospitalWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineOvarian cancerBRCA mutationSalpingectomyBreast cancerMalignancyHysterectomyGynecologySerous fluidOncologyEndometrial cancerPopulationInternal medicineCancerObstetricsSurgeryPregnancy

Abstract

fetched live from OpenAlex

Ovarian cancer (OC) is the leading cause of death among women with gynecologic malignancy. Breast Cancer Susceptibility Gene 1 (BRCA 1) and Breast Cancer Susceptibility Gene 2 (BRCA 2) germline mutations confer an estimated 20 to 40 times increased risk of OC when compared to the general population. The majority of BRCA-associated OC is identified in the late stage, and no effective screening method has been proven to reduce mortality. Several pharmacologic and surgical options exist for risk-reduction of gynecologic malignancy in BRCA 1/2 mutation carriers. This review summarizes up-to-date research on pharmacologic risk-reducing interventions, including the oral contraceptive pill, acetylsalicylic acid/nonsteroidal anti inflammatory drugs (ASA/NSAID) therapy, and denosumab, and surgical risk-reducing interventions, including risk-reducing bilateral salpingo-oophorectomy, salpingectomy with delayed oophorectomy, and hysterectomy at the time of risk-reducing bilateral salpingo-oophorectomy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.243
GPT teacher head0.466
Teacher spread0.223 · 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 designNot applicable
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

Citations6
Published2022
Admission routes2
Has abstractyes

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