Preclinical models of frailty: Focus on interventions and their translational impact: A review
Bibliographic record
Abstract
The concept of frailty refers to heterogeneity in the risk of adverse outcomes for people of the same age. It is traditionally thought of as the inability of the body to maintain homeostasis. It can help explain differences between chronological and biological age and can quantify healthspan in experimental studies. Although clinical studies have developed tools to quantify frailty over the past two decades, preclinical models of frailty have only recently been introduced. This review describes the notion of frailty and outlines two commonly used clinical approaches to quantify frailty: the frailty phenotype and the frailty index. Translation of these methodologies for use in animals is introduced and studies that use these models to evaluate interventions designed to attenuate or exacerbate frailty are discussed. These include studies involving manipulation of diet, implementation of exercise regimens and tests of pharmaceutical agents to exacerbate or attenuate frailty. Together, this body of work suggests that preclinical frailty assessment tools are a valuable new resource to quantify the impact of interventions on overall health. Future studies could deploy these models to evaluate new frailty therapies, test combinations of interventions and assess interventions to enhance the ability to resist stressors in the setting of ageing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".