Improved Assessment of Overall Health in Variably Aged Murine Models of Multiple Sclerosis With a Novel Frailty Index Tool
Bibliographic record
Abstract
The experimental autoimmune encephalomyelitis (EAE) model is the most commonly used animal model of multiple sclerosis (MS). However, phenotypic characterization of mice based on the traditional 5-point clinical paralysis scale does not fully capture disease progression. The frailty index (FI) conceptualizes frailty as the accumulation of health deficits and it is widely used to assess overall health in aging humans and preclinical models. Here, we adapted an established mouse FI tool for use in EAE mice and determined whether this could evaluate general signs of health in variably aged female EAE mice. The EAE-Clinical FI included 34 items related to clinical signs and deficits characteristic of aging and MS. This tool clearly showed more detailed EAE progression and severity at all ages, highlighting changes in systems other than motor paralysis measured with the traditional 5-point paralysis scale. When we induced disease at 3 and 6 months of age, mice showed typical EAE clinical manifestations with peak disease severity between 17 and 19 days post-induction and mean frailty scores of 0.36 ± 0.04 (3-month-old) and 0.43 ± 0.05 (6-month-old). By contrast, disease severity peaked after 14 days in 12-month-old mice. They showed atypical signs including wobbling, early belly drag, and splayed hindlegs that were better captured with the EAE-Clinical FI. Peak frailty scores also were higher than those of younger animals (0.54 ± 0.04). As MS most often develops in young to middle-aged people, this new tool may have significant value for use in EAE animal studies as a first step toward translation to people with MS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".