Abstract Improvement and decline in health status from late middle age: Modeling age-related changes in deficit accumulation
Bibliographic record
Abstract
In a prospective multi-panel cohort study, we investigated how, from late middle age, individuals ’ health status improves or declines. In the Canadian National Population Health Survey, transition probabilities between different health states were estimated for 4330 people (58.8 % women) aged 55+ at baseline over 2-year intervals from 1994 to 2000. Health status was defined by a deficit count, using 33 health-related variables combined in a frailty index. For each time interval, the chance of accumulating deficits increased linearly with the number of deficits. Older survivors (aged 70–85) showed a slightly lower chance of stability or improvement (52%; 95 % confidence interval 50–54%) compared with those in late middle age (56%; 54–58%). Changes in health states can be described with high accuracy (R 2 = 0.92) by a modified Poisson distribution, using four parameters: the background odds of accumulating additional deficits, the chance of incurring more or fewer deficits, given the existing number, and the corresponding probabilities of dying. An age-invariant limit to deficit accumulation was observed at 22 deficits. From late middle age, transitions in health states occur with a regularity that is easily modeled. Improvements in health can occur at any age. At all ages, there is a limit to deficit accumulation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".