Data Resource Profile: Heart Valve Society Aortic Valve Database (HVS AV Database)
Bibliographic record
Abstract
Background Multicenter, standardized, long-term data are essential for assessing outcomes of patients following aortic (valve) treatment. To facilitate this, the international Heart Valve Society (HVS) Aortic Valve (AV) Database was established. This paper provides an overview of the HVS AV Database. Methods The HVS AV Database includes adult patients with aortic valve disease (regurgitation and/or stenosis), with or without ascending aorta aneurysm, undergoing surgical or transcatheter intervention. It has an ambispective design, and centers interested in interventional outcomes following aortic (valve) intervention can participate free of charge. Results The variables collected in the database are harmonized with the International Consortium for Health Outcomes Measurement (ICHOM) standard Set of Patient-Centered Outcome Measures for Heart Valve Disease (HVD). In total, 33 outcome variables, including 12 clinical and 21 echocardiographic, and 286 case-mix variables are collected. Currently, the database includes 10,893 patients from 78 participating centers worldwide, with 7910 patients having undergone aortic valve repair and 1968 aortic valve replacement. Participating centers can extract their own data at any time, and multicenter research proposals are encouraged. The database is compliant with GCP, FDA, GDPR and HIPAA regulations, supporting data transfer across continents. Conclusions The HVS AV Database is aimed at improving outcomes for patients requiring intervention for aortic (valve) disease. Through uniform scientific reporting, it contributes to global efforts to standardize data capture, improve patient outcomes and shift the focus from device to global patient outcomes.
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 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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.107 | 0.069 |
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".