A NETWORK PERSPECTIVE OF AGING AND FRAILTY
Bibliographic record
Abstract
We computationally model aging individuals as a network of nodes representing health attributes. Nodes stochastically damage to form health deficits. Damage rates depend on the state of neighboring nodes to represent interactions between physiological systems. The Frailty Index (FI) is the proportion of health deficits in a set of health attributes, or the proportion of damaged nodes in a subset of the network. Our model demonstrates known patterns of human aging, such as Gompertz’s law of mortality and the average trajectory of the FI, without any assumption of programmed aging. We use our model to better understand factors that influence the health trajectories of individuals. Different types of health attributes can be represented in the model as different classes of network nodes. This allows us to model FI-clinical, a FI using self-reported or clinically diagnosable health attributes, and FI-lab, a FI using biomarkers or laboratory tests. Damage to nodes within these FIs affect mortality and the accumulation of deficits in different ways. Many differences in the behavior of the FI-clinical and FI-lab from the CSHA and NHANES studies can be explained in terms of the structure of the network underlying health attributes. More generally, our computational model allows us to understand how the damage occurring throughout an individual’s life influences their health and lifespan.
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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 | 0.001 |
| 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".