Aetiology, prevalence, and prognosis
Bibliographic record
Abstract
Tricuspid regurgitation (TR) is a common yet often overlooked valvular disorder that carries a substantial impact on morbidity and mortality. It is increasingly recognized as a heterogeneous entity with different phenotypes identified (primary, atrial secondary, ventricular secondary, and cardiac implantable electronic device-related). Contemporary population studies and disease-specific registries reveal that secondary TR is highly prevalent in elderly patients, those with heart failure of any phenotype, and in candidates for transcatheter aortic or mitral interventions. Prognosis varies widely according to aetiology, with atrial secondary TR consistently associated with better survival than ventricular secondary TR. Across diverse settings, TR severity is an independent predictor of mortality, and several clinical scores, including the TRI-SCORE, Wang score, and TRIO score, have been developed to refine risk stratification. Recent staging models integrating ventricular function, renal status, and biomarkers suggest that intervention during an intermediate disease phase, before irreversible end-organ damage, may optimize outcomes. Together, these advances underscore the need for accurate phenotyping, structured prognostic assessment, and timely intervention to improve the care of patients with TR.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".