Comparative Approaches to Understanding the Relation Between Aging and Physical Function
Bibliographic record
Abstract
Despite dedicated efforts to identify interventions to delay aging, most promising interventions yielding dramatic life-span extension in animal models of aging are often ineffective when translated to clinical trials. This may be due to differences in primary outcomes between species and difficulties in determining the optimal clinical trial paradigms for translation. Measures of physical function, including brief standardized testing batteries, are currently being proposed as biomarkers of aging in humans, are predictive of adverse health events, disability, and mortality, and are commonly used as functional outcomes for clinical trials. Motor outcomes are now being incorporated into preclinical testing, a positive step toward enhancing our ability to translate aging interventions to clinical trials. To further these efforts, we begin a discussion of physical function and disability assessment across species, with special emphasis on mice, rats, monkeys, and man. By understanding how physical function is assessed in humans, we can tailor measurements in animals to better model those outcomes to establish effective, standardized translational functional assessments with aging.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".