Prospective Assessment of Coronary Artery Flows Before and After Cardiopulmonary Bypass in Children With a Spectrum of Congenital Heart Disease
Bibliographic record
Abstract
Background Normative data for the effect of cardiopulmonary bypass (CPB) on coronary artery Doppler velocities by transesophageal echocardiography in paediatric patients with congenital heart disease (CHD) are lacking. The objective of the study was to prospectively examine the effects of CPB on coronary artery flow patterns by transesophageal echocardiography before and after CPB in children with CHD. Methods All cases undergoing CHD surgery at the Hospital for Sick Children, Toronto, were eligible. The excluded cases included Norwood operation, heart transplantation, or weight <2.5 kg. Coronary Dopplers and coronary flow reserve (CFR) for the right coronary artery (RCA) and left anterior descending (LAD) were obtained. Multivariable analyses using linear regression models were performed, adjusted for age and cross-clamp time. Results From May 2017 to June 2018, 69 children (median age at surgery: 0.7 years, interquartile range [IQR]: 0.4-3.7 years; median weight: 7.4 kg, IQR: 5.8-13.3 kg) were included. They were grouped into shunt lesions (N = 26), obstructive lesions (N = 26), transposition of the great arteries (N = 5), and single ventricle (N = 12). N = 39 (57%) were primary repairs, and 56 (81%) had 1 CPB run. For RCA and LAD peak velocities, there was an increase from pre- to post-CPB in RCA peak 39 cm/s (IQR: 30-54 cm/s) to 65 cm/s (IQR: 47-81 cm/s), P < 0.001, mean CFR 1.52 (IQR: 1.25-1.81), and LAD peak 49 cm/s (IQR: 39-60 cm/s) to 70 cm/s (IQR: 52-90 cm/s), P < 0.001, mean CFR 1.48 (IQR: 1.14-1.77). Conclusions Coronary flow velocities increase from pre- to post-CPB in congenital heart lesions. CFR is consistent across all lesions but is relatively low compared with the adult population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".