P2–386: Frailty in relation to neuropathological markers of Alzheimer's disease: Evidence from a transgenic mouse model
Bibliographic record
Abstract
In humans, frailty, quantified by the frailty index, is associated with risk of all causes of late-life cognitive impairment, including Alzheimer's disease (AD). This study investigated the relationship between a frailty index and neuropathological features of AD in a transgenic mouse model. Eleven male APPSWE/PSEN1de9 mice (6–13.5 mos), which exhibit accumulation of amyloid plaque similar to humans, were investigated. The frailty index was created by combining 24 potential health deficits (e.g. abnormal activity levels, % fat, bone mineral density, blood glucose). Each mouse's frailty index score was the proportion of deficits relative to the total number of measures. The association between the frailty index, age, and amyloid-beta plaque load was tested using correlations. The relationship between the frailty index and amyloid-beta plaque load was tested for an exponential fit. The frailty index and age were significantly correlated (r=0.75, p=0.009). Amyloid-beta plaque load was significantly correlated with the frailty index (r=0.73, p=.0.01). While there was a trend toward plaque load increasing with age, this correlation was insignificant (p=0.09) Plaque load increased exponentially in relation to increasing values of the frailty index. Amyloid-beta plaque load appears to have a stronger relationship with the frailty index than with age. The mechanism(s) by which neuropathological disease burden is associated with frailty regardless of chronological age requires further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".