Abstract 18301: Intra-operative Left Coronary Doppler Profile Independently Predicts Need for Ecmo or Late Revision After Arterial Switch
Bibliographic record
Abstract
Objective: We hypothesized that intraoperative coronary Doppler profiles after button implantation would help predict survival and morbidity after arterial switch. Methods: Children (N=319) undergoing arterial switch (2000-2012; TGA 190, TGA-VSD 95, TGA-VSD-PS 10, Taussig-Bing 14, others 10) underwent intraoperative echocardiographic assessment of coronary buttons. Studies were blindly reviewed to delineate Doppler gradient, peak flow velocity, velocity-time integral (VTI) and late diastolic flow reversal, which were explored as predictors of death, ICU morbidity and need for late coronary revision via parametric risk-adjusted regression and bootstrap reliability. Results: Outcomes included: death=9 (3%), ECMO=13 (4%), surgical coronary revision=9 (3%). Higher left button Doppler values were predictors of chest open duration, post-op JET, need for revision or ECMO. Mean left button gradient was the most reliable - table. Right button Doppler values were not strong predictors of morbidity. Higher left Doppler gradients confer disproportionate impact - figure. Mean gradient ≥ 1.5 had late revision risk 27%, versus 1.6% for < 1.5 ( P =.0016). Intraoperative strategies to improve button geometry included release of fascia (16, 5%) or button revision (9, 3%). Such strategies reduced gradients, velocities and VTI ( P between .004 and .03 ). Left Doppler values retained their predictive value even in 296 (93%) children who had no such coronary manipulation - table. Conclusions: Aiming for mean left coronary gradient < 1.5-2 and peak <4 mmHg through routine Doppler assessment will help lower risk of ECMO and late coronary problems.
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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 | 0.003 |
| 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) | 0.005 | 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".