Prevalence and 10‐Year Outcomes of Frailty in Older Adults in Relation to Deficit Accumulation
Bibliographic record
Abstract
OBJECTIVES: To evaluate the prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. DESIGN: Prospective cohort study. SETTING: The National Population Health Survey of Canada, with frailty estimated at baseline (1994/95) and mortality follow-up to 2004/05. PARTICIPANTS: Community-dwelling older adults (N=2,740, 60.8% women) aged 65 to 102 from 10 Canadian provinces. During the 10-year follow-up, 1,208 died. MEASUREMENTS: Self-reported health information was used to construct a frailty index (Frailty Index) as a proportion of deficits accumulated in individuals. The main outcome measure was mortality. RESULTS: The prevalence of frailty increased with age in men and women (correlation coefficient=0.955-0.994, P<.001). The Frailty Index estimated that 622 (22.7%, 95% confidence interval (CI)=21.0-24.4%) of the sample was frail. Frailty was more common in women (25.3%, 95% CI=23.2-27.5%) than in men (18.6%, 95% CI=15.9-21.3%). For those aged 85 and older, the Frailty Index identified 39.1% (95% CI=31.3-46.9%) of men as frail, compared with 45.1% (95% CI=39.7-50.5%) of women. Frailty significantly increased the risk of death, with an age- and sex-adjusted hazard ratio for the Frailty Index of 1.57 (95% CI=1.41-1.74). CONCLUSION: The prevalence of frailty increases with age and at any age lessens survival. The Frailty Index approach readily identifies frail people at risk of death, presumably because of its use of multiple health deficits in multidimensional domains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".