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Participation of Women in Cardiovascular Trials From 2017 to 2023

2025· review· en· W4413856259 on OpenAlexaff
Frederick Berro Rivera, John Vincent Magalong, Nathan Ross B. Bantayan, Nicole Ann Tesoro, Mark Jason Milan, Vikramjit Purewal, Polyn Luz S Pine, Chieh-Mei Tsai, Ann Marie Návar, Sharon L. Mulvagh, James L. Januzzi, C. Michael Gibson, Anuradha Lala-Trindade, Kyla Lara-Breitinger, Mayra Guerrero, Martha Gulati

Bibliographic record

VenueJAMA Network Open · 2025
Typereview
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsQueen Elizabeth II Health Sciences Centre
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesAbiomedCedars-Sinai Medical CenterNovo NordiskCytokineticsLadies Hospital Aid SocietyAbbott DiagnosticsAstraZenecaEli Lilly and CompanyEdwards LifesciencesNational Institutes of HealthU.S. Department of Health and Human ServicesU.S. Department of Defense
KeywordsMedicine

Abstract

fetched live from OpenAlex

Importance: Cardiovascular (CV) disease is the leading cause of death globally for both men and women, yet women remain historically underrepresented in CV clinical trials, despite facing a disproportionately high burden of morbidity and mortality in many forms of CV disease. Objective: To determine the representation of women across a broad range of CV trials. Evidence Review: The participation of women in CV trials registered on ClinicalTrials.gov from 2017 to 2023 was systematically determined through the extraction of publicly available information. Data were extracted to identify the country of study, disease type, trial size, clinical intervention, and age of the participants. The proportion of women and the ratio of number of female to male participants (F:M ratio) were calculated for each trial. The women's participation:prevalence ratio (PPR) was estimated for each trial based on the relative prevalence of the disease by sex in the specified region. Findings: A total of 1079 registered CV trials were identified, including 1 396 104 participants, of whom 571 641 (41.0%) were women. The F:M ratio was significantly lower for studies on arrhythmia (median [IQR], 0.5), coronary heart disease (median [IQR], 0.39 [0.33-0.70]), acute coronary syndrome (median [IQR], 0.32 [0.24-0.51]), and heart failure (median [IQR], 0.51 [0.32-0.87]) but higher for obesity (median [IQR], 1.44 [1.08-4.50]) and pulmonary hypertension (median [IQR], 2.86 [1.50-3.97]) trials. The F:M ratio was higher for trials on lifestyle interventions (median [IQR], 1.51 [0.77-3.24]) than for drug trials. PPRs were low for clinical trials on coronary heart disease (median [IQR], 0.66 [0.50-0.86]), acute coronary syndrome (median [IQR], 0.79 [0.51-0.87]), and stroke (median [IQR], 0.74 [0.61-0.95]). Representation of women in CV trials varied by disease state, region, intervention, and sponsor type. Conclusions and Relevance: These findings highlight both progress and persistent challenges in representation of women within CV trials. These gaps not only limit the generalizability of trial outcomes but also perpetuate inequities in evidence-based care for women with CV conditions.

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)0.170
metaresearch head score (Gemma)0.316
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1700.316
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.312
GPT teacher head0.494
Teacher spread0.182 · 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.

Study designSystematic review
DomainMethods
GenreReview

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

Citations24
Published2025
Admission routes1
Has abstractyes

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