Bibliographic record
Abstract
Recently discovered interventions that target fundamental aging mechanisms have been shown to increase life span in mice and other species, and in some cases, these same manipulations have been shown to enhance health span and alleviate multiple age-related diseases and conditions. Aging is generally associated with decreases in resilience, the capacity to respond to or recover from clinically relevant stresses such as surgery, infections, or vascular events. We hypothesize that the age-related increase in susceptibility to those diseases and conditions is driven by or associated with the decrease in resilience. Thus, a test for resilience at middle age or even earlier could represent a surrogate approach to test the hypothesis that an intervention delays the process of aging itself. For this, animal models to test resilience accurately and predictably are needed. In addition, interventions that increase resilience might lead to treatments aimed at enhancing recovery following acute illnesses, or preventing poor outcomes from medical interventions in older, prefrail subjects. At a meeting of basic researchers and clinicians engaged in research on mechanisms of aging and care of the elderly, the merits and drawbacks of investigating effects of interventions on resilience in mice were considered. Available and potential stressors for assessing physiological resilience as well as the notion of developing a limited battery of such stressors and how to rank them were discussed. Relevant ranking parameters included value in assessing general health (as opposed to focusing on a single physiological system), ease of use, cost, reproducibility, clinical relevance, and feasibility of being repeated in the same animal longitudinally. During the discussions it became clear that, while this is an important area, very little is known or established. Much more research is needed in the near future to develop appropriate tests of resilience in animal models within an aging context. The preliminary set of tests ranked by the participants is discussed here, recognizing that this is a first attempt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".