Prevalence and Spectrum of Conditions Associated with Severe Tricuspid Regurgitation
Bibliographic record
Abstract
BACKGROUND: Data regarding the prevalence and spectrum of conditions associated with severe tricuspid regurgitation (TR) are limited to small cohorts. METHODS: We retrospectively identified all patients with severe native tricuspid valve regurgitation in a large echocardiogram database between January 2004 and December 2010. Patients were classified into 1 of 3 groups based on the echocardiogram results: (1) organic TR; (2) functional TR; and (3) idiopathic TR. RESULTS: Severe TR was identified in 768 (1.2%, 72 ± 16 years, 64.3% females) of 63, 472 consecutive patients referred for transthoracic echocardiography. The conditions associated with severe TR could be established in 91% of patients. The remaining 9% were classified as idiopathic severe TR with these patients being older (78 ± 10 years) and having a higher frequency of atrial fibrillation (63.8%) compared to patients with organic (65 ± 22 years; 31%) or functional severe TR (73 ± 16 years; 47.8%). Overall, organic severe TR was identified in 11.3% of all cases. Functional severe TR occurred in 79.7% of the overall cohort and was related to pulmonary hypertension and/or left-sided heart disease. CONCLUSION: Severe TR occurred with a prevalence of 1.2% in our patients referred for echocardiography and was more common in females. Functional severe TR was the most common etiology with only a minority of cases secondary to organic severe TR. Idiopathic severe TR was found in a small proportion of patients who were older and more likely to have atrial fibrillation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".