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Record W7118851199 · doi:10.1051/medsci/2025230

Du silence aux données

2025· article· fr· W7118851199 on OpenAlexaboutno aff
A. Granger Vallee, Jean-Marc Ayoubi

Bibliographic record

Venuemédecine/sciences · 2025
Typearticle
Languagefr
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSilencePopulationQuarter (Canadian coin)CohortMental healthDigital health

Abstract

fetched live from OpenAlex

The menopausal transition concerns over a quarter of the female population in France, but its clinical and socio-professional trajectories remain poorly explored. CLIMATÈRE is a prospective, fully digital cohort study designed to recruit over 100,000 women, aged 30 and over (whether premenopausal, perimenopausal, or menopausal). Participants register on the CLIMATÈRE website to complete questionnaires covering menopausal symptoms, lifestyle factors, mental health, and occupation. Annual follow-up will be conducted. CLIMATÈRE will provide a national map of women's health at the time of menopause, with the goal of developing personalized prevention tools in the field of women's 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 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)0.020
metaresearch head score (Gemma)0.145
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: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.145
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0060.007
Scholarly communication0.0160.010
Open science0.0030.007
Research integrity0.0060.017
Insufficient payload (model declined to judge)0.0540.038

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 head0.074
GPT teacher head0.376
Teacher spread0.302 · 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
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
Published2025
Admission routes1
Has abstractyes

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