Measures of Healthspan as Indices of Aging in Mice—A Recommendation
Bibliographic record
Abstract
Over the past decade, a large number of discoveries have shown that interventions (genetic, pharmacological, and nutritional) increase the lifespan of invertebrates and laboratory rodents. Therefore, the possibility of developing antiaging interventions for humans has gone from a dream to a reality. However, it has also become apparent that we need more information than just lifespan to evaluate the translational potential of any proposed antiaging intervention to humans. Information is needed on how an intervention alters the "healthspan" of an animal, that is, how the physiological functions that change with age are altered. In this report, we describe the utility and the limitations of assays in mice currently available for measuring a wide range of physiological functions that potentially impact quality of life. We encourage investigators and reviewers alike to expect at minimum an overall assessment of health in several domains across several ages before an intervention is labeled as "increasing healthspan." In addition, it is important that investigators indicate any tests in which the treated group did worse or did not differ statistically from controls because overall health is a complex phenotype, and no intervention discovered to date improves every aspect of health. Finally, we strongly recommend that functional measurements be performed in both males and females so that sex differences in the rate of functional decline in different domains are taken into consideration.
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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 | 0.001 | 0.000 |
| 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".