Implementation of the mouse frailty index
Bibliographic record
Abstract
Frailty is considered a state of high vulnerability for adverse health outcomes for people of the same age. Those who are frail have higher mortality, worse health outcomes, and use more health care services than those who are not frail. Despite this, little is known about the biology of frailty, the effect of frailty on pharmacological or surgical outcomes, and potential interventions to attenuate frailty. In humans, frailty can be quantified using a frailty index (FI) based on the principle of deficit accumulation. The recent development of an FI in naturally ageing mice provides an opportunity to conduct frailty research in a validated preclinical model. The mouse FI has been successfully used across a wide range of applications; however, there are some factors that should be considered in implementing this tool. This review summarises the current literature, presents some original data, and suggests areas for future research on the current applications of the mouse FI, inter-rater reliability of the FI, the effect of observer characteristics and environmental factors on mouse FI scores, and the individual items that make up the FI assessment. The implementation of this tool into preclinical frailty research should greatly accelerate translational research in this important field.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".