Abstract 20276: Tetralogy of Fallot With Hypoplastic Branch Pulmonary Arteries: Aggressive Patch Augmentation Improves Short-Term Geometry but Increases Risk of Late Re-Interventions
Bibliographic record
Abstract
Introduction: When encountering hypoplastic branch pulmonary arteries (PA) during tetralogy of Fallot repair (TOF), strategies include: 1) patch augment to hilum (PATCH), 2) extending patches into proximal part only (EXTEND), or 3) leave native vessels uninstrumented in anticipation of growth (NATIVE). We tested outcomes for these opposing strategies. Methods: We studied all 434 TOF repairs (2000-12, excluding pulmonary atresia). Risk-adjusted models analyzed competing endstates of branch PA reintervention or death. PA growth was explored via repeated measures analysis of 2123 echo measurements. Subgroup analysis of children with branch PA Results: Overall survival was excellent (99%; 3 deaths). Mean freedom from catheter or surgical re-intervention to branch PAs at 10 years was 84%. In models risk-adjusted for baseline features (including PA size), PATCH augmentation of branch PAs was associated with significantly higher rates of re-intervention (75% freedom; p Branch PA In PATCH(28), EXTEND(60) and REPAIR(75) groups, freedom from re-intervention adjusted for BPA z-score was 60%, 70% and 80% respectively, and patient characteristics were similar (figure). More PATCH children had received PA stents (P=.04), but the majority of all groups had not (figure). Aggressive PATCH strategy was associated with decreased time-related branch PA growth (p=.02, 667 echos). Conclusions: Aggressive patch augmentation of branch PAs improves short-term geometry but may lead to late stenosis and higher rates of re-intervention. Hypoplastic branch PAs in TOF tend to grow well in their native state or with minimal surgical manipulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".