UNDERSTANDING AGING AND FRAILTY WITH A PREDICTIVE NETWORK MODEL
Bibliographic record
Abstract
Abstract Health deficits are age-related binary health issues (typically self-reported disabilities) that accumulate with age. Acquiring a deficit makes an individual more frail and susceptible to other associated deficits. We model this process as a network of health deficits that interact with each other. Mortality depends on an individual’s current deficits and their age. The model is trained with self-reported data from the Canadian Study of Health and Aging (CSHA) or the National Health and Nutrition Examination Survey (NHANES). The model generates longitudinally data for synthetic-individuals with frailty trajectories and mortality resembling the observed data. We verify this by comparing the prevalence of individual deficits, correlations between deficits, and predicted death ages with test data. Our trained model performs well on all of these measures. Our model informs our understanding of aging by providing an interaction network representing the associations between pairs of deficits. Our model can generate the frailty trajectories of individuals starting from a set of deficits at a given age. This can extrapolate the trajectories of observed individuals to older ages and enables “inducing” or “treating” deficits to understand the effects of individual deficits or sets of deficits on health.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".