Abstract 13178: A Novel, Data-Driven Approach to Classify Critical Left Ventricular Outflow Tract Obstruction Using Pre-Intervention Echocardiographic Measurements: A Report From The Congenital Heart Surgeons’ Society Data Center
Bibliographic record
Abstract
Objective: Critical left ventricular outflow tract obstruction (LVOTO) has been traditionally classified by aortic and mitral valvar pathology. We aimed to determine if baseline qualitative and quantitative echocardiographic measures alone could define new groups of patients with greater clinical relevance. Methods: Pre-intervention transthoracic echocardiograms for 651 neonates with Critical LVOTO were interpreted by one pediatric cardiologist according to a standardized protocol. Cluster analysis, with 136 echocardiographic measures, was used to group the patients. Variables defining each group were identified by multinomial regression. Results: Cluster analysis categorized the 651 neonates into groups of 215 (Group 1), 338 (Group 2), and 98 (Group 3) patients (Panel A). Aortic valve atresia and left ventricular (LV) end diastolic volume were identified as significant discriminating variables. LV size was largest in Group 3 and smallest in Group 2. Aortic atresia was most prevalent in Group 2 and least prevalent in Group 3. The distribution of these and other variables is shown in Panel B. Balloon valvotomy was the first intervention in 9% (19/215), 2% (6/338), and 61% (60/98) (p<0.0001). In those with an initial operation, single ventricle palliation was performed in 90% (176/215), 98% (326/338), and 58% (22/38) (p<0.0001). Overall mortality in each group was 27% (59/215), 41% (138/338), and 12% (12/98) (p<0.0001). Conclusions: Using a completely data-driven approach, we identified three novel groups, primarily based on baseline LV size, that correlate with management strategy and overall mortality. These groups roughly correspond anatomically with multi-level LV hypoplasia, hypoplastic left heart syndrome, and critical aortic stenosis, respectively. Our analysis suggests that a more useful classification of critical LVOTO may require more detailed measurements, especially of LV size, than a simplistic scheme limited to valvar pathology.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".