The biology of frailty in humans and animals: Understanding frailty and promoting translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it