Opening Up the Future of Valvular Heart Disease
Bibliographic record
Abstract
With an aging population, the prevalence of valvular heart disease (VHD) has risen steadily and is expected to continue increasing in the coming decades. In parallel, the remarkable development of transcatheter therapies has dramatically expanded both the therapeutic landscape and the number of patients eligible for curative interventions. Together, these trends have created a growing financial, societal, and logistical burden on health care systems worldwide. Despite technological breakthroughs, major challenges persist. Many patients with VHD remain undiagnosed, are referred late in the disease course, and ultimately receive suboptimal treatment. Unlike atherosclerosis or heart failure, where medical therapies have led to major clinical advances, there is currently no effective medical therapy to slow or prevent degenerative VHD progression. Health care systems face significant constraints, including shortages of health care providers, limited access to primary care, restricted availability of diagnostic testing, and cardiology services. These challenges are particularly pronounced particularly in publicly funded systems such as Canada, where direct specialist access is limited, and hospital capacity is insufficient. These challenges are unevenly distributed: individuals with low socioeconomic status or those living in rural or underserved areas bear the greatest burden. Moreover, structured and coordinated health care pathways for valve disease remain lacking. Addressing these challenges requires opening up the future of valve disease: expanding early detection beyond hospital walls, integrating digital and community-based care, fostering big data and innovation networks, and accelerating the development of medical therapies and personalized medicine approaches through multidisciplinary collaboration that can truly transform patient outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".