Strain measurement for 2D echo with ultrasound enhancing agents can be performed with software available for non-enhanced recordings
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): ABACUS Cardiovascular Research Centre, Mazankowski Alberta Heart Institute Background Global longitudinal strain (GLS) measurement are less reliable when 2D image quality is reduced. While ultrasound enhancing agents (UEA; also known as "echo contrast") have been shown to enhance the reliability of EF measurements, there has been sparse evidence on performing GLS measurements after UEA injection. The aim of this study was to assess the feasibility of GLS analysis using the same software validated for non-enhanced 2D recordings. Methods GLS measurements were performed before and after injection of an approved UEA in 131 patients with acceptable image quality for GLS measurements. These patients were referred prior to chemotherapy initiation or were being monitored for potential cardiotoxic effects. As all these patients undergo echocardiography with UEA in order to achieve the best reproducibility of EF measurements, comparison of GLS measurements with and without UEA was possible. A commercially available ultrasound system was used and the same analysis software was applied. On end-diastolic and end-systolic frames, the inner border of the region of interest was manually adjusted to align with interface between the compact and trabeculated myocardium on non-enhanced images and the LV blood pool on the recordings with UEA. Results GLS measurements on recordings with UEA were performed in 131 patients. A strong positive correlation (r 0.67, p < 0.001) was found between measurements on non-enhanced recordings with mean bias 0.58% (see figure). Mean GLS was 19.5 +/- 2.1% from non-enhanced recordings and 20.1 +/- 2.2% from UEA recordings. Differences in GLS > 2% between methods were related to foreshortening or suboptimal delineation of segments on non-contrast recordings. Conclusion On a commercially available echocardiography scanner, software developed for GLS measurements on non-enhanced 2D recordings can be also applied to recordings that use UEA. Comparable results are obtained, provided the LV cavity is well delineated and not foreshortened. Abstract Figure 1
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.018 |
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".