Tracking changes in frailty throughout later life: results from a 17-year longitudinal study in the Netherlands
Bibliographic record
Abstract
Background: to better understand the development of frailty with ageing requires longitudinal studies over an extended time period. Objective: to investigate changes in the degree of frailty during later life, and the extent to which changes are determined by socio-demographic characteristics. Methods: six measurement waves of 1,659 Dutch older adults aged 65 years and over in the Longitudinal Aging Study Amsterdam (LASA) yielded 5,211 observations over 17 years. At each wave, the degree of frailty was measured with a 32-item frailty index (FI), employing the deficit accumulation approach. Socio-demographic characteristics included age, sex, educational level and partner status. Generalized Estimating Equation (GEE) analyses were performed to study longitudinal frailty trajectories. Results: higher baseline FI scores were observed in older people, women, and those with lower education or without partner. The overall mean FI score at baseline was 0.17, and increased to 0.39 after 17 years. The average doubling time in the number of deficits was 12.6 years, and this was similar in those aged 65-74 years and those aged 75+. Partner status was associated with changes over time in FI score, whereas sex and educational level were not. Conclusions: this longitudinal study showed that the degree of frailty increased with ageing, faster than the age-related increase previously observed in cross-sectional studies. Even so, the rate of deficit accumulation was relatively stable during later life.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".