Aging as a Process of Deficit Accumulation: Its Utility and Origin
Bibliographic record
Abstract
Individuals of the same age differ greatly with respect to their health status and life span. We have suggested that the health status of individuals can be represented by the number of health deficits that they accumulate during their life. We have suggested that this can be measured by a fitness-frailty index (or just a frailty index), which is the ratio of the deficits present in a person to the total number of deficits considered (e.g. available in a given database or experimental procedure). Further, we have proposed that the frailty index represents the biological age of the individual, and suggested an algorithm for its estimation. In investigations by many groups, the frailty index has shown reproducible properties such as: age-specific, nonlinear increase, higher values in women, strong association with mortality and other adverse outcomes, and universal limit to its increase. At the level of individual, the frailty index shows complex stochastic dynamics, reflecting both stochasticity of the environment and the ability to recover from various illnesses. Most recently, we have proposed that the origin of deficit accumulation lies in the interaction between the environment, the organism and its ability to recover. We apply a stochastic dynamics framework to illustrate that the average recovery time increases with age, mimicking the age-associated increase in deficit accumulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".