Electrocardiogram pattern of some exotic breeds of trained dogs: A variation study
Bibliographic record
Abstract
AIM: The present study has been conducted to evaluate the variation in electrocardiogram (ECG) parameters among different trained breeds of dogs (viz. Labrador, German Shepherd, and Golden Retriever) used for security reasons. MATERIALS AND METHODS: The ECG was recorded by single channel ECG at a paper speed of 25 mm/s and calibration of 10 mm=1 mV. The recordings were taken from all the standard bipolar limb leads (Lead-I, II, and III) and unipolar augmented limb leads (Lead-aVR, aVL, and aVF). RESULTS: Heart rate was found to be highest in Labrador and lowest in German Shepherd. P-wave duration was maximum in Golden Retriever breed and lowest in Labrador. Maximum amplitude of P-wave was found in Labrador followed by German Shepherd and Golden Retriever. There was significantly (p<0.05) higher values of PR interval in German Shepherd compared to other breeds. The variation in QRS duration, ST segment duration, T-wave duration, and T-wave amplitude was found to be non-significant among breeds. Inverted T-waves were most common in Golden Retriever and German Shepherd, whereas positive T-waves were found in Labrador. There was significant (p<0.05) variation in mean electrical axis of QRS complex among different breeds and it ranges from +60° to +80°. CONCLUSION: The present study provides the reference values for different ECG parameters to monitor the cardiac health status among Labrador, German Shepherd, and Golden Retriever breeds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".