Aging health dynamics cross a tipping point near age 75
Bibliographic record
Abstract
Aging includes both continuous gradual decline, such as in physiological function, together with major deficit onset events such as morbidity, disability and ultimately death. These deficit events are stochastic and include non-linear feedbacks, making health trajectory forecasting challenging. We propose a framework for modelling the gradual effects of aging together with health deficit onset events, as reflected in the frailty index (FI) - a quantitative measure of overall age-related health. We model damage and repair dynamics of the FI from individual health transitions within two large longitudinal studies of aging health, the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA), which together included N = 47592 individuals. We find that both damage resistance (robustness) and damage recovery (resilience) rates decline smoothly with both increasing age and with increasing FI, for both sexes. This leads to two distinct dynamical states: a robust and resilient young state of stable good health (low FI) and an older state that drifts towards poor health (high FI). These two health states are separated by a sharp transition near age 75. Since FI accumulation risk accelerates dramatically across this tipping point, ages 70-80 are crucial for understanding and forecasting late-life decline in health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".