Intra-individual variability in cognitive performance predicts falls in older adults with chronic stroke
Bibliographic record
Abstract
BACKGROUND: Common consequences of a stroke include impaired motor and cognitive function, with both being linked to increased falls and frailty. Intra-individual variability (IIV) of cognitive performance, which refers to the within-person trial-to-trial variation in reaction time during cognitive tasks, may be a useful predictor for falls in older adults with chronic stroke. OBJECTIVE: To examine whether IIV or "traditional" reaction time (RT) measures of cognitive performance predict falls in older adults with chronic stroke. METHODS: This study is a secondary analysis of a proof-of-concept randomized controlled trial (RCT) among community-dwelling adults with a history of stroke, aged 55 years and older, able to walk 6 m, and without dementia. Residualised intraindividual standard deviation (rISD) was the measure of IIV and mean RT was the "traditional" measure of performance on a computerised Stroop Task. Falls were tracked and adjudicated over six months. RESULTS: 120 participants with a mean (SD) age of 70 (8) years, and 46 (38%) female participants, experienced a mean of 0.61 (SD = 1.15) falls over 6 months. rISD for the congruent Stroop Task condition predicted falls, such that a one-unit increase was associated with 20.5% increase in fall rate. CONCLUSION: The findings suggest that IIV metrics may have the potential in fall risk screening post-stroke. Further research is required to evaluate whether IIV in cognitive performance can be improved via interventions such as cognitive training and physical activity.
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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 | 0.007 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".