Frailty Assessment in Animal Models
Bibliographic record
Abstract
Although frailty has been extensively investigated for the last 2 decades, preclinical models of frailty have only been developed over the past decade. Frailty is a concept that helps to explain the difference between chronologic age and biologic age and to discuss health span along with lifespan. In general, a frail individual will be more susceptible to adverse health outcomes than a healthy, nonfrail individual of the same age. However, the biology and mechanisms of frailty are still unclear. The development of preclinical models of frailty and frailty assessment tools are invaluable to geriatric research. This review briefly describes the concept of frailty and discusses the newly developed animal models of frailty, specifically the frailty phenotype- and frailty index-based models. Mouse models are the most common models for preclinical frailty research, but rat and canine models for frailty assessment have also been developed. These models can facilitate the testing of frailty-specific treatments and help to investigate the effects of various interventions on frailty. Similarities and differences between human and animal models, including sex differences in frailty, are also discussed. The availability of animal models of frailty is a valuable and welcome addition to the study of frailty, aging, or the disorders of old age and will enable a better understanding of frailty mechanisms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".