Modelling the Age Dynamics of Chronic Health Conditions: Life-Table-Consistent Transition Probabilities and Their Application
Bibliographic record
Abstract
Background: Surveys of chronic health conditions provide information about prevalence but not about the incidence and the process of change within the population. Objective: We show how the “age dynamics” of chronic conditions ‐‐ the probabilities of contracting the conditions at different ages, of moving from one chronic conditions state to another, and of dying ‐‐ can be inferred from prevalence data for those conditions that can be viewed as irreversible. Methods: Transition probability matrices are constructed for five‐year age groups, representing the age dynamics of health conditions for a stationary population. We illustrate the application of the matrices by simulating the age/health path of an initially healthy cohort. Results and conclusion: Surveys of chronic conditions provide valuable information about prevalence rates; we show that such surveys can be made even more valuable by allowing the calculation of the transition probabilities that define the chronic conditions age dynamic process. We report the results of simulations based on transition probabilities that we have derived, and note the general applicability of the methods.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".