Cardiology's best friend: Using naturally occurring disease in dogs to understand heart disease in humans
Bibliographic record
Abstract
Heart diseases are a leading cause of death globally. Laboratory and preclinical animal models of disease have been critical in advancing our understanding of the mechanisms of pathology, creating diagnostic tools, and developing therapeutic interventions. However, fundamental biological dissimilarities between humans and rodents limits their usefulness in research, and the induction of disease in an otherwise healthy animal creates unrealistic conditions under which diseases are typically studied. Dogs are at high risk of acquiring and dying from several naturally occurring heart disorders that also affect people. The spontaneous nature of these conditions, along with highly similar cardiovascular systems, offers unique opportunities to investigate cardiovascular disease in a more relevant model for humans. This review focuses on three common cardiac conditions that impact humans and dogs: dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, and mitral valve disease - comparing mechanisms of disease, diagnostics, and treatments, to identify strengths and present limitations of their utility. It is noted that the benefits of this research are bidirectional, with the potential to translate knowledge and clinical tools used in veterinary medicine to human patients, and vice versa.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".