Late gestation predictors of a postnatal biventricular circulation after fetal aortic valvuloplasty
Bibliographic record
Abstract
OBJECTIVES: Fetal aortic valvuloplasty (FAV) for severe aortic stenosis (AS) has shown promise in averting progression to hypoplastic left heart syndrome. After FAV, predicting which fetuses will achieve a biventricular (BiV) circulation after birth remains challenging. Identifying predictors of postnatal circulation on late gestation echocardiography will improve parental counseling. METHODS: Liveborn patients who underwent FAV and had late gestation echocardiography available were included (2000-2017, n = 96). Multivariable logistic regression and classification and regression tree analysis were utilized to identify independent predictors of BiV circulation. RESULTS: Among 96 fetuses, 50 (52.1%) had BiV circulation at the time of neonatal discharge. In multivariable analysis, independent predictors of biventricular circulation included left ventricular (LV) long axis z-score (OR 3.2, 95% CI 1.8-5.7, p < 0.001), LV ejection fraction (OR 1.3, 95% CI 1.0-1.8, p = 0.023), anterograde aortic arch flow (OR 5.0, 95% CI 1.2-20.4, p = 0.024), and bidirectional or right-to-left foramen ovale flow (OR 4.6, 95% CI 1.4-15.8, p = 0.015). CONCLUSION: Several anatomic and physiologic parameters in late gestation were found to be independent predictors of BiV circulation after FAV. Identifying these predictors adds to our understanding of LV growth and hemodynamics after FAV and may improve parental counseling.
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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 | 0.000 | 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 | 0.001 |
| 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".