Mobility screening for fall prediction in the Canadian Longitudinal Study on Aging (CLSA): implications for fall prevention in the decade of healthy ageing
Bibliographic record
Abstract
BACKGROUND: Guidelines for fall prevention in older adults recommend mobility screening for fall risk assessment; however, there is no consensus on which test to use and at what cutoff. This study aimed to determine the accuracy and optimal cut-off values of commonly used mobility tests for predicting falls in the Canadian Longitudinal Study on Aging (CLSA). METHODS: Mobility tests at baseline included the Timed Up and Go (TUG), Single Leg Stance (SLS), chair-rise and gait speed. Inclusion criteria were: age ≥ 65 years and meeting first-level fall screening criteria (i.e. history of a fall or mobility problem) at baseline. Accuracy of fall prediction at 18-months for each test was measured by the area under the receiver operating curve (AUC). RESULTS: Of 1,121 participants that met inclusion criteria (mean age 75.2 ± 5.9 years; 66.6% women), 218 (19.4%) reported ≥one fall at 18 months. None of the tests achieved acceptable accuracy for identifying individuals with ≥one fall at follow-up. Among women 65-74 and 75-85 years, the TUG identified recurrent fallers (≥two falls) with optimal cut-off scores of 14.1 and 12.9 s (both AUCs 0.70), respectively. Among men 65-74 years, only the SLS showed acceptable accuracy (AUC 0.85) for identifying recurrent fallers with an optimal cutoff of 3.6 s. CONCLUSIONS: Our findings indicate that commonly used mobility tests do not have sufficient discriminability to identify fallers in a population-based sample of community-dwelling older adults. The TUG and SLS can identify recurrent fallers; however, their accuracy and cut-off values vary by age and sex.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".