Prevalence of arrhythmias in dogs examined between 2008 and 2014
Bibliographic record
Abstract
INTRODUCTION: The prevalence of arrhythmias in dogs and the influence of sex, breed, age, and body weight were analysed over a seven-year span. MATERIAL AND METHODS: In total, 1189 referrals for cardiological examination by electrocardiography were received at one academic centre in Poland between 2008 and 2014. The largest proportion of the examined dogs were cross-breeds with body weight below 25 kg (n = 153, 12.87%), followed by German Shepherds (n = 122, 10.26%), Labrador Retrievers (n = 68, 5.72%), Yorkshire Terriers (n = 63, 5.3%), and Boxers (n = 60, 5.05%). Retrospective analysis was made of 1201 standing or right recumbent electrocardiograms without pharmacological sedation. The prevalence of arrhythmias was examined in terms of sex, age, body weight, and breed of the dogs. RESULTS: A total of 630 (52.46%) electrocardiograms showed no signs of arrhythmia, but 96 (7.99%) and 475 (39.55%) pointed to physiological and pathological arrhythmias respectively. The most commonly diagnosed type was atrial fibrillation with 33.68% incidence, followed by ventricular arrhythmias (28%), sinus pauses (27.58%), supraventricular arrhythmias (24%), and atrioventricular blocks (22.95%). Pathological arrhythmias were most commonly found in male dogs and in German Shepherds. CONCLUSIONS: Atrial fibrillation predominated, followed by premature ventricular complexes. Male dogs were generally more prone to heart rhythm disturbances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".