Frailty among middle-aged and older Canadians: population norms for the frailty index using the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: frailty is a public health priority now that the global population is ageing at a rapid rate. A scientifically sound tool to measure frailty and generate population-based reference values is a starting point. OBJECTIVE: in this report, our objectives were to operationalize frailty as deficit accumulation using a standard frailty index (FI), describe levels of frailty in Canadians ≥45 years old and provide national normative data. DESIGN: this is a secondary analysis of the Canadian Longitudinal Study on Aging (CLSA) baseline data. SETTING/PARTICIPANTS: about 51,338 individuals (weighted to represent 13,232,651 Canadians), aged 45-85 years, from the tracking and comprehensive cohorts of CLSA. METHODS: after screening all available variables in the pooled dataset, 52 items were selected to construct an FI. Descriptive statistics for the FI and normative data derived from quantile regressions were developed. RESULTS: the average age of the participants was 60.3 years (95% confidence interval [CI]: 60.2-60.5), and 51.5% were female (95% CI: 50.8-52.2). The mean FI score was 0.07 (95% CI: 0.07-0.08) with a standard deviation of 0.06. Frailty was higher among females and with increasing age, and scores >0.2 were present in 4.2% of the sample. National normative data were identified for each year of age for males and females. CONCLUSIONS: the standardized frailty tool and the population-based normative frailty values can help inform discussions about frailty, setting a new bar in the field. Such information can be used by clinicians, researchers, stakeholders and the general public to understand frailty, especially its relationship with age and sex.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".