Real-Time 3-Dimensional Echocardiography Provides New Insight Into Mechanisms of Tricuspid Valve Regurgitation in Patients With Hypoplastic Left Heart Syndrome
Bibliographic record
Abstract
BACKGROUND: Tricuspid regurgitation in hypoplastic left heart syndrome has an impact on outcome, but its mechanisms remain unclear. METHODS AND RESULTS: Real-time 3-dimensional echocardiography was performed in 35 patients with hypoplastic left heart syndrome (age, 1 month to 10 years; 10 after first-stage Norwood, 12 after superior cavopulmonary shunt, 13 after Fontan). From the 3-dimensional data set, we marked the annulus in systole and diastole. At mid systole, we marked the location of the papillary muscle tip and point of chordal attachment to the leaflet. We traced the surfaces of the tricuspid valve leaflets and measured the volume of leaflet prolapse, tethering, annular and septal leaflet areas, and papillary muscle position. Seventeen patients had moderate tricuspid regurgitation (prolapse, 7; tethered leaflets, 7) and 18 mild (prolapse, 0; tethered leaflets, 7). Multiple linear regression analysis revealed that moderate tricuspid regurgitation is associated with leaflet tethering and prolapse; that in hypoplastic left heart syndrome with tethered leaflets, the papillary muscle is displaced laterally and the tricuspid annulus is more planar; and that enlargement of the annulus at mid systole, small septal leaflet area, and age affect the degree of prolapse. CONCLUSIONS: In hypoplastic left heart syndrome, moderate tricuspid regurgitation may be associated with increasing age, geometrical changes of the annulus, leaflet prolapse, lateral papillary muscle displacement, and subsequent leaflet tethering, as well as a smaller septal leaflet.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".