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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".