Modelling the Age Dynamics of Chronic Health Conditions: Life-Table-Consistent Transition Probabilities and their Application
Bibliographic record
Abstract
Surveys of chronic health conditions provide information about prevalence but not incidence and the process of change within the population. Our study shows how "age dynamics" of chronic conditions – the probabilities of contracting conditions at different ages, of moving from one chronic condition state to another, and of dying – can be inferred from prevalence data for those conditions that can be viewed as irreversible. Transition probability matrices are constructed for successive age groups, with the sequence representing the age dynamics of the health conditions for a stationary population. We simulate the life path of a cohort under the initial probabilities, and again under altered probabilities, to explore the effects of reducing the incidence or mortality rate associated with a particular condition. We show that such surveys of chronic conditions can be made even more valuable by allowing the calculation of the transition probabilities that define the chronic conditions aging process.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".