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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".