Borderline left ventricular hypoplasia without significant aortic or mitral stenosis: cardiac magnetic resonance criteria for biventricular repair
Bibliographic record
Abstract
OBJECTIVES: Bi-ventricular (Bi-V) repair is viable in some patients with borderline left ventricular (LV) hypoplasia. We aimed to identify cardiovascular magnetic resonance (CMR) criteria predictive of successful primary Bi-V repair in neonates with borderline LV hypoplasia without significant mitral valve (MV) & aortic valve (AV) stenosis. METHODS: Retrospective study (2003-2024) of patients with borderline LV hypoplasia with CMR for decision-making. Patients MV stenosis (mean Doppler gradient >5 mmHg) and/or AV stenosis (peak Doppler gradient >20 mmHg) were excluded. Patients were divided into two groups: primary Bi-V repair and hybrid procedure. Outcomes were categorized as successful primary Bi-V repair, successful staged Bi-V repair and failure to achieve Bi-V repair. RESULTS: About 23/37 patients (62%) underwent successful primary Bi-V repair, 8/37 (22%) underwent staged Bi-V repair and 6/37 (16%) failed to achieve Bi-V repair. The successful primary/staged Bi-V repair group had higher LVEDVi (P < 0.002), higher blood flow volume through the ascending aorta (P < 0.012) and higher QAo/superior vena cava (QSVC) flow ratio (P = 0.034) compared to the failure to achieve Bi-V repair group. CMR LVEDVi cut-off of 27 ml/m2 had 87% sensitivity and 79% specificity (AUC 87.6%), and QAo threshold of 1.9 l/min/m2 had 65.2% sensitivity and 92.9% specificity (AUC 86.0%) for predicting successful primary Bi-V repair. About 7/31(22%) patients with Bi-V repair underwent reinterventions for LVOT obstruction & MV stenosis. CONCLUSIONS: In patients with borderline LV hypoplasia without MV/AV stenosis, CMR LVEDVi > 27 ml/m2 & QAo > 1.99 l/min/m2 were prognostic for successful primary biventricular repair.
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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 | 0.000 |
| 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".