The current state of myocardial contrast echocardiography: what can we read between the lines?
Bibliographic record
Abstract
Myocardial contrast echocardiography (MCE) is advocated for the assessment of myocardial perfusion in addition to wall motion during stress echocardiography for the diagnosis of coronary artery disease.1,2 We therefore read with interest the article by Bhattacharyya et al.3 on behalf of the British Society of Echocardiography detailing the current status and performance of stress echocardiography in the UK. In particular, we note the limited performance of MCE with only a small number of units (10.5%) using this technique, and a corresponding ‘under-utilization’ of vasodilator stress. Given the emerging literature,4–6 the observed uptake of MCE in clinical practice remains low, and despite continued enthusiasm of its proponents, the rate of progress integrating this modality has been a source of frustration.7 In a publicly funded health service that prioritizes efficiency alongside quality, the shorter stress times facilitated by the use of vasodilator stress would seem attractive, yet the authors found that adenosine and dipyridamole were used in 7 and 13% of units, respectively, compared with dobutamine in 100%.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".