Measurements of transmural strain variations by two dimensional ultrasound speckle tracking
Bibliographic record
Abstract
Background: Myocardial infarction (MI) is known to progress from the inner layers towards the epicardium. Since it is important to detect MI early, to prevent a negative remodeling process of the left ventricle (LV), the hypothesis of this study was that evaluation of layer-specific strains is feasible, and it will enable differentiation between subjects with large MI, small MI and normal LV. Methods: In this study a commercial speckle tracking echocardiography (STE) program was modified to measure the strains at three myocardial layers instead of for the total-wall-thickness. After a validation process by using software implemented phantoms, the commercial and modified programs were applied to echocardiography of 54 subjects. Results: The validation study results for 972 segments showed an agreement between the endocardial strains, manually measured by the commercial program, and automatically measured by the modified program. Finally, the algorithm was applied to scans of 15 normal subjects, 9 patients with small MI and to 6 patients with large MI. The results show that the strain elevated from the endocardium towards the epicardium for the normal and small MI groups, but not for the large MI group. Conclusions: In conclusion, the layer-specific STE method allows accurate analysis of the transmural variations of the strains.
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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 | 0.003 |
| 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 | 0.000 |
| 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".