The development and validation of an index to predict 10-year mortality risk in a longitudinal cohort of older English adults
Bibliographic record
Abstract
Background: we aimed to develop and validate a population-representative 10-year mortality risk index for older adults in England. Methods: data were from 10,798 men and women aged 50 years and older in the population-based English Longitudinal Study of Ageing in 2002/03, randomly split into development (n = 5,377) and validation cohorts (n = 5,421). Participants were asked about their sociodemographics, health behaviours, comorbidities, and functional status in the home-based interviews. Variables that were independently associated with all-cause mortality through March 2013 in the development cohort were weighted relative to one another to develop risk point scores for the index that was calibrated in the validation cohort. Results: the validated 10-year mortality risk index assigns points for: increasing age (50-59 years: 0 points; 60-64: 1 point; 65-69: 3 points; 70-74: 5 points; 75-79: 7 points; 80-84: 9 points; ≥85: 12 points), male (2 points), no vigorous physical activity (1 point), smoking (2 points), having a diagnosis of cancer (1 point), chronic lung disease (2 points) or heart failure (4 points), and having difficulty preparing a hot meal (2 points), pushing or pulling large objects (1 point) or walking 100 yards (1 point). In the full study cohort, 10-year mortality rates increased from 1.7% (11/664) in those with 0 points to 95% (189/199) among those with ≥16 points. Conclusion: this highly predictive 10-item mortality risk index is valid in the English population aged 50 years and older. It uses simple information that is often available in research studies and patient reports, and does not require biomarker data to predict mortality.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".