THE MOUSE CLINICAL FRAILTY INDEX AS AN OUTCOME AND MODULATOR OF DRUG THERAPY
Bibliographic record
Abstract
Frailty can be quantified in people by counting the accumulation of deficits in health (signs, symptoms, diseases) to construct a “frailty index” (FI). We have quantified frailty in naturally-ageing mice by counting differences in >30 health-related variables (hemodynamics, blood work, activity, body composition). We showed that 30 month-old mice had higher FI scores than 12 month-old animals (0.43 ± 0.03 vs 0.08 ± 0.02; p<0.001; n=12). Similar results were obtained when FI scores were calculated based on clinically-apparent signs of deterioration in mice. Mice treated with known longevity interventions (caloric restriction, resveratrol) had lower FI scores than untreated controls. Importantly, the relationship between FI scores and age (normalized to 90% mortality) was similar in mice and humans; the highest scores were close to the submaximal frailty limit of 0.67 in humans. This ability to quantify frailty in animals will help understand the biology of frailty and provide a platform to test new clinical interventions.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".