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Record W7118852440 · doi:10.1093/jsxmed/qdaf350

The role of erectile function in heterosexual relationship dynamics: a mixed methods study on women’s perspectives

2025· article· en· W7118852440 on OpenAlexaff
Emily Nham, Kiera Liblik, Humberto Vigil, Ryan Flannigan, Lori A. Brotto, Luke Witherspoon

Bibliographic record

VenueThe Journal of Sexual Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsVancouver Coastal HealthVancouver General HospitalKingston Health Sciences CentreRockyview General HospitalKingston General HospitalOttawa Hospital
Fundersnot available
KeywordsErectile functionErectile dysfunctionSexual functionFunction (biology)MEDLINE

Abstract

fetched live from OpenAlex

Globally, an estimated 322 million male adults are affected by erectile dysfunction (ED). Almost half of male adults in their 40s have experienced ED, and this increases with each decade of life.1 Patients with ED report a reduced quality of life and higher healthcare utilization.1 Due to the heterogeneity of ED, patients may need to navigate several treatment options, including psychosocial support, medications, injectables, and surgery.1 Several studies have explored the experiences of cisgender women with partners with ED.1-3 A cross-sectional survey study assessed the association between sexual function in cisgender women and men. Overall, women with partners who had ED scored lower on the Female Sexual Function Index than those with partners without ED.2 Another cross-sectional study identified key areas where men and women partners held divergent beliefs. For example, 25% of women agreed with the statement “this problem has been devastating for me in some ways,” compared to 39% of men.3 These studies focused on sexual function rather than the global relationship impact of ED. Our objective was to investigate how cisgender women perceive the importance of erections in their relationships, and how ED influences intimacy, sexual satisfaction, and relationship dynamics.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.381
Teacher spread0.348 · 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 designQualitative
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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