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Record W4411078064 · doi:10.1192/bjp.2025.109

Clarifying our language for better women’s brain health: what do we really mean when we say ‘menopause’ and ‘hormone therapy’?

2025· article· en· W4411078064 on OpenAlexafffund
Noelia Calvo, Gillian Einstein

Bibliographic record

VenueThe British Journal of Psychiatry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsBaycrest HospitalPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMenopausePleaHormone therapyPostmenopausal womenMedicinePsychologyInternal medicineBreast cancerPolitical scienceCancer

Abstract

fetched live from OpenAlex

Interest in women's brain health has grown rapidly. However, the terms 'menopause' and 'hormone therapy' have been used as general concepts embracing different types of menopause and treatments. In this editorial, we make a plea for accurate description of each type to generate precision evidence.

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)0.014
metaresearch head score (Gemma)0.057
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0090.008
Open science0.0040.001
Research integrity0.0170.039
Insufficient payload (model declined to judge)0.0040.003

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 head0.006
GPT teacher head0.265
Teacher spread0.259 · 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
GenreCommentary

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

Citations2
Published2025
Admission routes2
Has abstractyes

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