Frailty: Scaling from Cellular Deficit Accumulation?
Bibliographic record
Abstract
Cells age in association with deficit accumulation via mechanisms that are far from fully defined. Even so, how deficits might scale up from the subcellular level to give rise to clinically evident age-related changes can be investigated. This 'scaling problem' can be viewed either as a series of little-related events that reflect discrete processes--such as the development of particular diseases--or as a stochastic process with orderly progression at the systems level, regardless of which diseases are present. Some recent evidence favors the latter hypothesis, but determining the best approach to study how deficits scale remains a key goal for understanding aging. In consequence, approaching the problem of frailty as one of the scaling of subcellular deficits has implications for understanding aging. Considering the cumulative effects of many small deficits appears to allow for the observation of important aspects of the behavior of systems that are close to failure. Mathematical modeling offers useful possibilities in clarifying the extent to which different clinical scales measure different phenomena. Even so, to be useful, mathematical modelling must be clinically coherent in addition to mathematically sound. In this regard, queuing appears to offer some potential for investigating how deficits originate and accumulate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".