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E088 Investigating the impact of menopause and its treatment on women with systemic autoimmune rheumatic diseases: the Menopause MATTERs Mixed Methods Research Project

2025· article· en· W4409899314 on OpenAlexaff
K. V. Naidu, Arvind Kaul, Zoe McLaren, Laura Andréoli, Lynn Holloway, Sydnae Taylor, Martha Piper, Wendy Diment, Edward Trenah, Felix Naughton, Thomas J. Reilly, David D’Cruz, Melanie Sloan

Bibliographic record

VenueLara D. Veeken · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMenopauseMedicinePostmenopausal womenObstetricsInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background/Aims Menopause represents a significant and often overlooked transition in the lives of women, particularly those with systemic autoimmune rheumatic diseases (SARDs), such as lupus. Hormonal fluctuations during menopause may exacerbate SARDs symptoms, complicate disease management, and diminish quality of life (QoL). Despite these potential implications, the interaction between menopause and SARDs remains underexplored in clinical practice. This is particularly important for rheumatologists, who must navigate complex patient cases where hormonal shifts may directly influence disease activity and treatment outcomes. Understanding these dynamics could lead to more personalised care strategies that improve patient outcomes. Our exploratory work revealed that rheumatologists often lacked confidence in this aspect of care. A previous study suggesting that HRT may cause lupus to flare was cited by some rheumatologists as a reason for advising against HRT, yet patient and clinician experiences are rarely directly sought. The menopause MATTERs (Menopause And TreaTment Experiences in Rheumatic diseaseS) study aims to investigate the impact of menopause and its treatment on people with and without systemic autoimmune rheumatic diseases. Objectives 1. To understand and compare the impact of menopause on SARDs patients and healthy people. 2. To investigate the influence of hormone replacement therapy (HRT) on SARDs symptoms and QoL. 3. To assess the satisfaction with menopause-related advice, support, and treatment among SARDs patients and healthy people. Methods An exploratory mixed-methods approach will be employed across 3 workstreams: Workstream 1: Investigates and compares the physical and psychosocial impact of menopause on SARDs patients with healthy controls, using validated instruments including the menopause rating scale (MRS), depression (PHQ8) and anxiety (GAD-7) indexes, and qualitative interview data. Workstream 2: Examines the effect of HRT on SARDs symptoms and QoL using both quantitative measures (e.g., t-tests, linear regression) and qualitative analyses. Workstream 3: Summarises patient satisfaction with menopause advice and treatment using descriptive statistics and thematic analysis. Results The Menopause MATTERs survey will launch in November 2024. Surveys will be distributed online to SARD patients, the general population and clinicians. Anticipated minimum response rates based on our previous research are n = 1000 for SARD patients, n = 250 for clinicians, and n = 400 for the general population. Data analysis will commence in January 2025, incorporating both qualitative and quantitative methods to explore the effect of menopause and HRT on disease activity and QoL, and satisfaction with menopause-related advice. Conclusion We anticipate highly novel findings from this research which will provide essential insights to guide more nuanced, individualised care for women managing both menopause and chronic autoimmune diseases. A summary of findings from all workstreams will be presented at the conference. Disclosure K. Naidu: None. A. Kaul: None. Z. Mclaren: None. L. Andreoli: Consultancies; Consultancy fees from Eli Lilly, Glaxo Smith Kline, Janssen, Novartis, UCB, and Werfen Group. L. Holloway: None. S. Taylor: None. M. Piper: None. W. Diment: None. E. Dunbar: None. L. Gallagher: None. E. Trenah: None. F. Naughton: None. B. Sloan: None. T. Reilly: None. D. D’Cruz: Corporate appointments; A position on the APS charity leadership board. Consultancies; consultancy/speaker fees from GSK, Eli Lilly, Vifor and UCB. M. Sloan: None.

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)0.042
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.003

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 head0.054
GPT teacher head0.408
Teacher spread0.354 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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