MétaCan
Menu
Back to cohort
Record W4387081224 · doi:10.1212/wnl.0000000000207845

Association of Cumulative Lifetime Exposure to Female Hormones With Cerebral Small Vessel Disease in Postmenopausal Women in the UK Biobank

2023· article· en· W4387081224 on OpenAlexafffund
Samantha Côté, Thomas-Louis Perron, Jean‐Patrice Baillargeon, Christian Bocti, Jean‐François Lepage, Kevin Whittingstall

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMedicineMenopauseMenarcheCohortHormone replacement therapy (female-to-male)Cohort studyPhysiologyGynecologyObstetricsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Rates of cerebrovascular disease increase after menopause, which is often attributed to the absence of hormones. It remains unknown whether the cumulative exposure to hormones across a female person's premenopausal life extends the window of cerebrovascular protection to the postmenopausal period. To investigate this, we examined the relationship between lifetime hormone exposure (LHE) and cerebral small vessel disease in more than 9,000 postmenopausal women in the UK-Biobank. METHODS: . RESULTS: > 0.05). DISCUSSION: Women with more prolonged exposure to endogenous hormones show relatively smaller burden of cerebral small vessel disease independent of the history of oral contraceptive use or hormone replacement therapy. Our results highlight the critical role endogenous hormones play in female brain health and provide real-world evidence of the protective effects premenopausal endogenous hormone exposure plays on postmenopausal cerebrovascular health.

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)0.000
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 score0.017
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.026
GPT teacher head0.294
Teacher spread0.269 · 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

Citations28
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueNeurologySame topicMenopause: Health Impacts and TreatmentsFrench-language works237,207