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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it