Prevalence of frailty in Canadians 18–79 years old in the Canadian Health Measures Survey
Bibliographic record
Abstract
BACKGROUND: There is little certainty as to the prevalence of frailty in Canadians in younger adulthood. This study examines and compares the prevalence of frailty in Canadians 18-79 years old using the Accumulation of Deficits and Fried models of frailty. METHODS: The Canadian Health Measures Study data were used to estimate the prevalence of frailty in adults 18-79 years old. A 23-item Frailty Index using the Accumulation of Deficits Model (cycles 1-3; n = 10,995) was developed; frailty was defined as having the presence of 25% or more indices, including symptoms, chronic conditions, and laboratory variables. Fried frailty (cycles 1-2; n = 7,353) included the presence of ≥3 criteria: exhaustion, physical inactivity, poor mobility, unintentional weight loss, and poor grip strength. RESULTS: The prevalence of frailty was 8.6 and 6.6% with the Accumulation of Deficits and the Fried Model. Comparing the Fried vs. the Accumulation of Deficits Model, the prevalence of frailty was 5.3% vs. 1.8% in the 18-34 age group, 5.7% vs. 4.3% in the 35-49 age group, 6.9% vs. 11.6% in the 50-64 age group, and 7.8% vs. 20.2% in the 65+ age group. Some indices were higher in the younger age groups, including persistent cough, poor health compared to a year ago, and asthma for the accumulation of deficits model, and exhaustion, unintentional weight loss, and weak grip strength for the Fried model, compared to the older age groups. CONCLUSIONS: These data show that frailty is prevalent in younger adults, but varies depending on which frailty tool is used. Further research is needed to determine the health impact of frailty in younger adults.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".