Abstract 15276: Echocardiographic Features Defining Right Ventricle Dominant Unbalanced Atrioventricular Septal Defect: a Multi-institutional Congenital Heart Surgeons' Society Study
Bibliographic record
Abstract
Objectives: Right ventricle (RV) dominant unbalanced atrioventricular septal defect (AVSD) remains challenging with regard to definition and management. We sought to assess which echo features best characterized patients based on functional and anatomic features. Methods: From a cohort of AVSD patients at 4 CHSS institutions (2000-2006), diagnostic echos of 60 randomly selected balanced patients and all 52 RV dominant patients (based on atrioventricular valve (AVV) index) underwent detailed review. Cluster analysis of these variables was used to group patients with similar features based on the echo data. Discriminant function analysis was used to determine which variables differentiated these groups. Results: Three groups were identified from the cluster analysis. Discriminant function analysis found that the echo variables that differentiated these groups were the RV:left ventricle (LV) inflow angle (RV/LVinflow), LV width/LV length, left AVV color diameter at smallest inflow, left AVV color diameter at annulus, right AVV overriding left atrium, and LV width. Outcomes associated with the 3 groups indicated that one group (1) represented balanced patients, while two groups (2,3) with similar outcomes represented unbalanced patients (Table). The dominant differentiating variable was RV/LVinflow (partial R2=0.86) (Figure), which we defined as the angle between the base of the RV and LV free wall, using the top of the ventricular septum as apex of the angle. Conclusions: The angle of RV/LVinflow is an important defining echo measure of RV dominance in patients with right unbalanced AVSD. The utility of this measure needs to be confirmed with a prospective study. ![Graphic][1] ![Graphic][2] [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".