A cross-national study of transitions in deficit counts in two birth cohorts: Implications for modeling ageing
Bibliographic record
Abstract
Generally, health does not improve with age, and many physical and physiological functions are known to decline. These changes do not occur uniformly, however; for many reasons, some people experience significant improvement in their health over non-trivial time intervals. Earlier, we showed that 5-year transitions in health status in elderly people (age 65+ years) can be modeled as a stochastic process, using a modified Poisson distribution with four readily interpretable parameters. The original description was based on follow-up of a single cross-sectional study, thus mixing age and cohort effects. Here, we again used a multistate Markov chain to model 5-year deficit accumulation in relation to frailty in both a Swedish birth cohort (aged 70 years at inception) and, from the original cross-sectional study, a Canadian birth cohort, aged 69–71. In both datasets, we found again that a modified Poisson describes the transition in health status with high precision. The parameters of the model though different, are close to each other, even though the cohorts are from different countries, were assembled 20 years apart, and counted different deficits. The model suggests that all health transitions, including health improvement, worsening, and death, can be summarized in a unified stochastic model with a few interpretable parameters.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".