Comparison of cardiac rotation measured by speckle tracking with an optical
Bibliographic record
Abstract
Background: Left ventricular (LV) rotation is an important component of cardiac function. There is little data on the comparison of Doppler speckle tracking with other invasive techniques for measuring LV rotation in the beating heart. We hypothesized that Doppler speckle tracking would compare favorably with data from an optical device that has previously been shown to measure LV rotation in the beating heart. Methods: Eight pigs with an open chest had LV rotation measured by Doppler speckle tracking and an optical device which was attached to the apex of the LV. Simultaneous hemodynamic data was obtained via Millar catheters, under a variety of loading conditions (inferior vena cava occlusion, dobutamine, as well as dobutamine and inferior vena cava occlusion). Results: Throughout the various hemodynamic changes, peak, late-systolic, early-diastolic and minimal rotation times correlated strongly between the two techniques (r = 0.93). Peak rotation occurred well after late-systole, with minimal rotation occurring uniformly during isovolumic contraction. Doppler speckle tracking rotation patterns corresponded closely with those obtained by the optical device. Conclusion: Doppler speckle tracking imaging appears to mirror findings obtained from an optical rotation device in a beating heart open-chested animal model throughout a variety of hemodynamic changes. This technique will provide further insight into the mechanisms of LV rotation in both congenital and acquired heart disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".