Intraoperative echocardiographic coronary artery imaging in congenital and acquired heart disease
Bibliographic record
Abstract
Transoesophageal and epicardial echocardiography are indispensible intraoperative imaging modalities to guide paediatric heart disease surgeries and influence surgical decision-making. A less well-described role of intraoperative imaging is its utility in evaluating coronary artery patency and flow. Focused two-dimensional, colour, and spectral Doppler imaging of the coronary arteries should be performed during surgeries involving coronary manipulation or re-implantation, or in cases where there is unexpected ventricular dysfunction or electrographic signs concerning for ischaemia. Intraoperative imaging allows for any anatomical issues to be detected and addressed promptly in the operating room. Imaging of the coronary arteries should identify unobstructed coronary ostia and proximal course without kinking, angulation, narrowing, or significant calibre change to suggest stenosis or extrinsic compression from neighbouring structures. The aim of this review is to highlight the usefulness of transoesophageal and epicardial echocardiography in evaluating coronary artery patency and flow, provide a how-to guide for optimal imaging, and to introduce a practical guideline to achieve best clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".