Challenges in the diagnosis and management of valve disease: the case for the specialist valve clinic
Bibliographic record
Abstract
Valvular heart disease (VHD) is responsible for a major societal and economic burden. Incidence and prevalence of VHD are high and increase as the population ages, creating the next epidemic. In Western countries, the etiology is mostly degenerative or functional disease and strikes an elderly population with multiple comorbidities. Epidemiological studies have shown that VHD is commonly underdiagnosed, leading to patients presenting late in their disease course, to an excess risk of mortality and morbidity and to a missed opportunity for intervention. Once diagnosed, VHD is often undertreated with patients unduly denied intervention, the only available curative treatment. This gap between current recommendations and clinical practice and the marked under-treatment is at least partially related to poor knowledge of current National and International Societies Guidelines. Development of a valvular heart team involving multidisciplinary valve specialists including clinicians, imaging specialists, interventional cardiologists and surgeons is expected to fill these gaps and to offer an integrated care addressing all issues of patient management from evaluation, risk-assessment, decision-making and performance of state-of-the-art surgical and transcatheter interventions. The valvular heart team will select the right treatment for the right patient, improving cost-effectiveness and ultimately patients' 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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".