The biology of frailty in humans and animals: Understanding frailty and promoting translation
Bibliographic record
Abstract
Frailty is a state of high vulnerability to adverse health outcomes. This concept is used to explain the heterogeneity in rates of aging in people of the same age. Frailty has important clinical implications, because even minor stressors can lead to adverse outcomes, including death, in frail individuals. Although frailty mechanisms are not well understood, advances in our ability to qualify frailty have encouraged efforts in this area. Quantification of frailty with both "frailty phenotype" and "frailty index" approaches has begun to highlight putative frailty mechanisms and new animal models of frailty are inspiring preclinical research. These models either adapt frailty phenotype and frailty index tools for use in animals or they use genetically manipulated mice that mimic conditions seen in frailty (eg, inflammation, sarcopenia, weakness). This review: describes commonly used tools to quantify frailty clinically, discusses potential frailty mechanisms, and describes animal models of frailty. It also highlights how these models have been used to explore frailty mechanisms and potential frailty interventions, including pharmacological treatments, diet, and exercise. These exciting new developments in the field have the potential to facilitate translational research, improve our understanding of mechanisms of frailty, and help develop new interventions to mitigate frailty in our aging population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".