How to Assess Frailty in Preclinical Models and Relate It to Clinical Paradigms
Bibliographic record
Abstract
Frailty is a state of increased risk for adverse health outcomes. Their degree of frailty makes frail individuals more susceptible to cardiovascular disease and mortality than are their fitter counterparts. Frailty can be quantified clinically in various ways. Most common are the frailty phenotype and frailty index. These have been reverse-translated for use in aging mice. This review describes these instruments and explains how they can quantify the degree of frailty in mouse models. Understanding the degree of frailty is critical, as frailty predicts cardiac morbidity and mortality better than do traditional risk factors for cardiovascular diseases.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".