The Association Between Self-Reported and Performance-Based Physical Function With Activities of Daily Living Disability in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Physical function limitations precede disability and are a target to prevent or delay disability in aging adults. The objective of this article was to assess the relationship between self-report and performance-based measures of physical function with disability. METHODS: Baseline data (2012-2015) from the Canadian Longitudinal Study on Aging (n = 51,338) was used. Disability was defined as having a limitation for at least one of 14 activities of daily living. Physical function was measured using 14 questions across three domains (upper body, lower body, and dexterity) and five performance-based tests (gait speed, timed up and go, single leg stance, chair rise, and grip strength). Logistic regression was used to assess the relationship between physical function operationalized as (i) at least one limitation, (ii) presence or absence of limitations in each individual domain/test, and (iii) number of domains/tests with limitations, with disability. RESULTS: In the 21,241 participants with self-reported function data, the odds of disability were 1.87 (95% CI: 1.56-2.24), 6.78 (5.68-8.08), and 14.43 (11.50-18.1) for one, two, and three limited domains, respectively. In the 30,097 participants with performance-based measures of function, the odds of disability ranged from 1.53 (1.33-1.76) for one test limited to 14.91 (11.56-19.26) for all five tests limited. CONCLUSIONS: Both performance-based and self-report measures of physical function were associated with disability. Each domain and performance test remained associated with disability after adjustment for the other domains and tests. Disability risk was higher when the number of self-report domains and performance-based limitations increased.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".