Generational Multimorbidity Disease Clusters for British Cohorts Born 1921 – 1960
Bibliographic record
Abstract
Abstract The aim of this study is the first step in our understanding of the uniqueness and stability of multmorbdity disease patterns for different generations. The unique historical context that each generation has been exposed to is thought to have systemic health impacts and differences in epidemiological make-up (Clouston et al. 2021). Literature suggests that multimorbidity disease patterns, are similar across countries (Hernandez et al, 2021 – in press) and observational points, and that migration into complex disease clusters is more common as people age (Cassell et al, 2018, Kingston et al. 2018). Most commonly reported are Cardiovascular and Metabolic disease clusters which lead to lower quality of life, mortality and morbidity (Kudesia, 2021). We asked: Do multimorbidity disease patterns differ for unique generations? Using the ELSA, the disease clusters of three cohorts were examined; an older cohort, born 1921-1930, a middle cohort born 1931-1940 a younger cohort born 1941-1950 and the ”newest” cohort, born 1951-1960. Self-reported dementia and memory problems lead a specific cluster for the middle cohort, those born in 1931-1940, but not for the other cohorts. While disease patterns were different between sex for other clusters, the disease cluster of dementia and memory problems held similar disease patterns for males and females, with a prevalence of 3%. The dementia/memory problem cluster loaded with cardio/metabolic diseases. This suggests that complex multimorbidity for the British 1931-1940 cohort has had an impact related to dementia and memory problem diagnoses for this specific generation, for males and females alike.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".