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Record W4281632563 · doi:10.1186/s13053-022-00227-z

Beyond the pill: contraception and the prevention of hereditary ovarian cancer

2022· review· en· W4281632563 on OpenAlexafffund
Yue Xia, Joanne Kotsopoulos

Bibliographic record

VenueHereditary Cancer in Clinical Practice · 2022
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersPeter Gilgan FoundationCanada Research Chairs
KeywordsMedicineOvarian cancerPillGynecologyPopulationFamily planningCancerHormonal contraceptionObstetricsOncologyInternal medicineEnvironmental healthPharmacologyResearch methodology

Abstract

fetched live from OpenAlex

BRCA1 and BRCA2 mutation carriers face an elevated lifetime risk of developing ovarian cancer. Oral contraceptives have been shown to significantly decrease the risk of ovarian cancer by approximately 50% in this high-risk population. Changes in contraceptive formulations and patterns of use over time have introduced lower hormonal dosages, different steroid types and non-oral routes of administration. Specifically, there has been a considerable shift in patterns of contraceptive use and the increase in the uptake of non-oral, long-acting, reversible contraception (e.g., intrauterine devices, implants, injections) has corresponded to a decline in oral contraceptive pill use. Whether or not these other methods confer a protective effect against ovarian cancer in the general population is not clear. To our knowledge, there have been no such studies conducted among BRCA mutation carriers. Furthermore, the impact of these changes on the risk of developing ovarian cancer is not known. In this article, we will review the existing epidemiologic evidence regarding the role of contraceptives and the risk of ovarian cancer with a focus on women with a BRCA1 or BRCA2 mutation. We will discuss recent findings and gaps in the knowledge while extrapolating from studies conducted among women from the noncarrier 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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.127
GPT teacher head0.475
Teacher spread0.348 · 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 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

Citations10
Published2022
Admission routes2
Has abstractyes

Explore more

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