Evaluation of Left Ventricular Function in Healthy Retrievers Using Standard and 2D Speckle-Tracking Echocardiography
Bibliographic record
Abstract
Standard echocardiography is vital for the assessment of cardiac performance in healthy and diseased animals. Similarly, two-dimensional speckle-tracking echocardiography (2D-STE) is an advanced echocardiographic technique that is becoming increasingly important for the assessment of myocardial function. Breeds, age, and body weight (BW) are known to be important factors affecting the echocardiographic parameters; therefore, the aim of this study was to evaluate the effect of breed, age, and BW on the echocardiographic parameters in three breeds of clinically healthy Retrievers. A total of 46 Retrievers, including 16 Flat-coated Retrievers (FR), 16 Golden Retrievers (GR), and 14 Labrador Retrievers (LR) were included in the study. The comparison of the breeds revealed significant differences in the LV wall thickness of FR and GR, although further analysis using MLR showed that the differences were most likely associated with BW, similarly to the other LV dimensions. Functional parameters, including ejection fraction, fractional shortening, and left-atrial-to-aortic ratio, were independent of breed, age, and BW. On the other hand, peak aortic blood flow velocity, trans-mitral rapid ventricular filling flow, and the ratio of trans-mitral rapid ventricular filling flow to atrial contraction were influenced by age. The 2D-STE-derived radial and circumferential strain parameters were independent of breed, age, and BW, except for global strain in the radial direction.
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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 | 0.001 |
| 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".