Considerations for choosing an optimal animal model of cardiovascular disease
Bibliographic record
Abstract
The decision to use the optimal animal model to mimic the various types of cardiovascular disease is a critical one for a basic scientist. Clinical cardiovascular disease can be complex and presents itself as atherosclerosis, hypertension, ischemia/reperfusion injury, myocardial infarcts, and cardiomyopathies, amongst others. This may be further complicated by the simultaneous presence of two or more cardiovascular lesions (for example, atherosclerosis and hypertension) and co-morbidities (i.e., diabetes, infectious disease, obesity, etc). This variety and merging of disease states creates an unusually difficult situation for the researcher who needs to identify the optimal animal model that is available to best represent all of the characteristics of the clinical cardiovascular disease. The present manuscript reviews the characteristics of the various animal models of cardiovascular disease available today, their advantages and disadvantages, with the goal to allow the reader access to the most recent data available for optimal choices prior to the initiation of the study. The animal species that can be chosen, the methods of generating these models of cardiovascular disease, as well as the specific cardiovascular lesions involved in each of these models are reviewed. A particular focus on the JCR:LA- cp rat as a model of cardiovascular disease is discussed.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".