Bibliographic record
Abstract
Animal models have contributed greatly to our understanding of the biology of aging and have been used to test new potential interventions to enhance survival. However, whether these interventions can modify frailty in animals is not yet clear, in part because until recently, frailty had not been considered in animal studies of aging. This review is focused on investigations that have attempted to address the issue of frailty, or aspects of frailty, in animal models, including invertebrate and vertebrate models. Some studies have used skeletal muscle weakness or sarcopenia as a surrogate for frailty in aging animals. Others have used genetically altered mice, in which components of human frailty such as inflammation are enhanced. This review also explores a novel approach to quantify frailty with a 'frailty index' based on deficit accumulation in aging animals. The concept of the frailty index is well established in the clinical literature, but recent work suggests that this approach can also be used to measure frailty in aging animals. The ability to quantify frailty in animals is a major step forward in the effort to understand the biology of frailty and to develop new clinical interventions.
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.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".