COGNITIVE PERFORMANCE DIFFERENTIALLY MODERATES THE ASSOCIATION BETWEEN GAIT VELOCITY AND FALL RISK
Bibliographic record
Abstract
Abstract Cognitive-walking dual-task paradigms are an effective tool for assessing fall risk. Surprisingly, the moderating influence of cognitive performance on the association between gait velocity (GV) and fall risk has not been explored. The present investigation examines whether cognitive performance moderates the relationship between GV and fall risk. Community-dwelling older adults (76 years ±3.44) were classified as fallers and non-fallers based on self-report (at least one fall in the past 12 months). GV was indexed using the GAITRite system while counting backwards by serial 7s. Performance on the serial 7s task was recorded, with Adobe Audition subsequently employed to index cognitive function in terms of the number of counts (NC) and percentage of true counts (PTC) from the recorded audio files. Logistic regression was employed to examine the predictive influence of GV on the likelihood of fall risk classification (GV model), as well as the moderating influence of NC and PTC on the GV-fall risk association (GCI model). Notably, there was a significant moderating effect of PTC on the GV-fall risk association for individuals with PTC scores ≥-2.41 units (Johnson-Neyman analysis); those with lower GV were at increased risk of falling, with this relationship magnified as PTC scores increased. The sensitivity of the GCI interaction model (88%) represented a 17% improvement over the GV model. This investigation is the first to demonstrate the importance of interactions between gait and cognition measures in fall risk modelling, and underscores the potential clinical utility of including cognitive performance measures in dual-task paradigms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".