Myocardial protection in paediatric cardiac surgery: building an evidence-based strategy
Bibliographic record
Abstract
Cardioplegia is fundamental to the surgical repair of congenital heart defects by protecting the heart against ischaemia/reperfusion injury, characterised by low cardiac output and troponin release in the early postoperative period. The immature myocardium exhibits structural, physiological and metabolic differences from the adult heart, with a greater sensitivity to calcium overload-mediated injury during reperfusion. Del Nido cardioplegia was designed specifically to protect the immature heart, is widely used in North America and may provide better myocardial protection in children; however, it has not been commercially available in the UK, where most centres use St Thomas' blood cardioplegia. There are no phase 3 clinical trials in children to support one solution over another and this lack of evidence, combined with variations in practice, suggests the presence of clinical equipoise. The best cardioplegia solution for use in children, and the impact of age and other clinical factors remain unknown. In this Hunterian lecture, I propose an evidence-based strategy to improve myocardial protection during cardiac surgery in children through: (1) conducting multicentre clinical trials of established techniques; (2) improving our knowledge of ischaemia/reperfusion injury in the setting of cardioplegic arrest; (3) applying this to drive innovation, moving beyond current cardioplegia solutions; (4) empowering personalised medicine, through combining clinical and genomic data, including ethnic diversity; and (5) understanding the impact of cardioplegic arrest on the late outcomes that matter to patients and their families.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".