The Valvular Heart Disease in Women (VHD-W) Registry: a global initiative to address gender disparities in management and outcomes
Bibliographic record
Abstract
Aims: Valvular heart disease is a leading cause of cardiovascular morbidity and mortality globally, with women experiencing delayed referrals, difficulties recognizing atypical symptoms, and suboptimal adherence to guideline-based therapies, resulting in worse outcomes. However, the literature identifying these disparities remains limited, underscoring the need for a comprehensive registry to address these gaps. The Valvular Heart Disease in Women Registry (VHD-W) aims to provide real-world insights into gender differences by examining treatment patterns, guideline adherence, and clinical results. Methods and results: The VHD-W is an international, multicenter, non-commercial, investigator-initiated, multipurpose registry endorsed by the European Association of Cardiovascular Imaging. The VHD-W involves adult patients with moderate-to-severe valvular heart disease admitted, either urgently or electively, to the cardiology inpatient service. The study aims to enrol 800 patients, balanced between genders, across more than 70 centres worldwide, over a 6-month period from the registry inception in March 2024 until the end of December 2025. Data will be collected at inpatient admission, inpatient discharge, and 1-year follow-up, including demographics, medical history, physical examination, biomarkers, echocardiography, other imaging results, and management. Conclusion The VHD-W is the first registry to focus on gender disparities in valvular heart disease in a real-world setting, aiming to fill a significant management gap that will help develop gender-specific, evidence-based guidelines for valvular heart disease.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".