Rat models of caloric intake and activity: relationships to animal physiology and human health
Bibliographic record
Abstract
Every rodent experiment is based on important parameters concerning the levels of caloric intake and physical activity. In many cases, these decisions are not made consciously, but are based on traditional models. For experimental models directed at the study of caloric intake and activity, the selection of parameters is usually aimed at modeling human conditions, the ultimate goal of which is to gain insight into the pathophysiology of the disease process in man. In each model, it is important to understand the influence of diet, exercise, and genetic background on physiology and the development of disease states. Along the continuum of energy intake from caloric restriction to high-fat feeding, and of energy output from total inactivity to forced exercise, a number of models are used to study different disease states. In this paper, we will evaluate the influence of the quantity and composition of diet and exercise in several animal models, and will discuss how each model can be applied to various human conditions. This review will be limited to traditional models using the rat as the experimental animal, and although it is not an exhaustive list, the models presented are those most commonly represented in the literature. We will also review the mechanisms by which each affects rat physiology, and will compare these to the analogous mechanisms in the modeled human disease state. We hope that the information presented here will help researchers make choices among the available models and will encourage discussion on the interpretation and extrapolation of results obtained from traditional and novel rodent experiments on diet, exercise, and chronic disease.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".