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Record W4407086276 · doi:10.3390/women5010004

Insights into Perimenopause: A Survey of Perceptions, Opinions on Treatment, and Potential Approaches

2025· article· en· W4407086276 on OpenAlexaboutno aff
Andrea K. Wegrzynowicz, Amanda C. Walls, Myra Godfrey, Amy Beckley

Bibliographic record

VenueWomen · 2025
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Science Foundation
KeywordsDemographicsPerceptionMenopauseMedicineDuration (music)GerontologyPsychologyDemographyInternal medicine

Abstract

fetched live from OpenAlex

Perimenopause, the transitional phase leading up to menopause, affects millions of women worldwide, yet it remains poorly understood and under-addressed in healthcare. Despite the availability of treatment options like hormone replacement therapy (HRT) and non-hormonal alternatives, the awareness and utilization of these options vary significantly among women. Here, we conducted a cross-sectional survey with 1000 adults, both men and women, from the United States and Canada. We evaluated the perceived familiarity of participants with the timing, duration, and symptoms of perimenopause, as well as their satisfaction with their treatment options and communication with their healthcare providers. We found that, in general, women and older people were more likely to feel familiar with perimenopause, although the youngest age group surveyed also reported relatively high familiarity. We also found that there is a disconnect between people reporting high familiarity with perimenopause and its symptoms but overall middling and lower familiarity with the age and duration of onset and satisfaction with treatment options. Our results suggest further investigation into where people obtain their information concerning perimenopause, as well as into how knowledge of perimenopause may vary based on demographics.

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 score0.477
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.062
GPT teacher head0.340
Teacher spread0.278 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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