Ventricular Tachyarrhythmias in 106 Cats: Associated Structural Cardiac Disorders
Bibliographic record
Abstract
BACKGROUND: Ventricular tachyarrhythmias occur in association with cardiac and extracardiac disorders in many species of animals, but information identifying concurrent disorders in cats with such arrhythmias is scarce. METHODS: We investigated coexisting diseases by retrospectively evaluating medical records of cats with ventricular tachyarrhythmias seen during a 51-month period at 1 institution. For comparative purposes, we evaluated records of dogs with similar arrhythmias during the same time period. All cats and dogs had premature ventricular complexes, accelerated idioventricular rhythm, ventricular tachycardia, or some combination of these arrhythmias, and all had undergone echocardiography during the same visit that led to the diagnosis of ventricular tachyarrhythmia. RESULTS AND CONCLUSIONS: Most (102/106; 96%) cats had at least 1 echocardiographically apparent abnormality concurrent with ventricular tachyarrhythmias. Ventricular tachyarrhythmias in cats were most commonly associated with myocardial disease (eg, left ventricular concentric hypertrophy [n = 66], restrictive or unclassified cardiomyopathy [n = 17], and dilated cardiomyopathy [n = 6]). When comparing dogs and cats that had ventricular tachyarrhythmias and were diagnosed on the same clinical service of the same institution, an echocardiographically apparent cardiac lesion was seen more often in cats (102/106, 96%) than in dogs (95/138, 69%) (P < .001).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".