Convergent validity and inter-rater reliability of a lower-limb multimodal physical function assessment in community-dwelling older adults
Bibliographic record
Abstract
Introduction: Lower-limb physical function declines with age and contributes to a greater difficulty in performing activities of daily living. Existing assessments of lower-limb function assess one dimension of movement in isolation or are not time-efficient, which discourages their use in community and clinical settings. We aimed to address these limitations by assessing the inter-rater reliability and convergent validity of a new multimodal functional lower-limb assessment (FLA). Methods: FLA consists of five major functional movement tasks (rising from a chair, walking gait, stair ascending/descending, obstacle avoidance, and descending to a chair) performed consecutively. A total of 48 community-dwelling older adults (32 female participants; age: 71 ± 6 years) completed the FLA as well as timed up-and-go, 30-s sit-to-stand, and 6-min walk tests. Results: Slower FLA time was correlated with a slower timed up-and-go test ( ρ = 0.70), less sit-to-stand repetitions ( ρ = −0.65), and a shorter distance in the 6-min walk test ( ρ = −0.69; all, p < 0.001). Assessments by two raters were not different (12.28 ± 3.86 s versus 12.29 ± 3.83 s, p = 0.98; inter-rater reliability ρ = 0.993, p < 0.001) and were statistically equivalent (via equivalence testing). Multiple regression and relative weights analyses demonstrated that FLA times were most predicted by the timed up-and-go performance [adjusted R 2 = 0.75; p < 0.001; raw weight 0.42 (95% CI: 0.27, 0.53)]. Discussion: Our findings document the high inter-rater reliability and moderate-strong convergent validity of the FLA. These findings warrant further investigation into the predictive validity of the FLA for its use as an assessment of lower-limb physical function among community-dwelling older 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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".