Comparative study of echocardiographic parameters in healthy and dilated cardiomyopathy-affected dogs.
Bibliographic record
Abstract
Echocardiography is a non-invasive and gold standard imaging tool for diagnosing dilated cardiomyopathy (DCM) in dogs. This study aimed to compare the echocardiographic parameters between healthy and DCM-affected dogs. A total of 52 client-owned dogs, comprising 38 males and 14 females, were included. Among these, 24 dogs (46.15%) were classified as healthy controls and 28 dogs (53.85%) were part of DCM group. On breed-wise prevalence, it was reported that Labrador Retriever breeds showed a higher incidence of DCM than the others. The comparative studies of echocardiographic parameters showed that DCM-affected dogs had significantly higher values in left ventricular long axis length at -end diastole (LVLdA4C) and -end systole (LVLsA4C), end diastolic volume (EDV), end systolic volume (ESV), left atrium (LA)/aorta diameter (Ao) ratio, left ventricular internal dimension at systole (LVIDs), and end point septal separation (EPSS), as well as significantly lower values in left ventricular contractibility indices such as fractional shortening (FS) and ejection fraction (EF) compared to healthy dogs. Also, receiver operating characteristic curves were made to determine the optimal cut-off points for each echocardiographic parameter with specificity and sensitivity for diagnosing DCM. Significant areas under the curve were observed for parameters such as LVIDs, EF, FS, LA/Ao, EPSS, LVLdA4C, LVLsA4C, left ventricular EDV, left ventricular ESV, and ESV for DCM-affected dogs. This cut-off value can be used as an early diagnosis of DCM through echocardiography, facilitating timely clinical interventions and management strategies for improved quality of life in dogs.
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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) | 0.000 | 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".