Cognitive Correlates of a Large Time Differential between Timed Up and Go and Gait Speed during In-Patient Stroke Rehabilitation
Bibliographic record
Abstract
Purpose: Cognitive impairment is highly prevalent after stroke but can be difficult to identify acutely. We aimed to study if a large difference between two common, routine physical therapy assessments (timed up-and-go [TUG] test and 10-metre walk test [10MWT]) could identify patients with subtle cognitive difficulties post-stroke. Method: An observational study was conducted in 141 individuals admitted to acute in-patient rehabilitation after stroke. We computed the per cent difference between TUG and 10MWT performance. Cognitive outcome measures were the trail making tests A and B (TMT-A and TMT-B) and the Functional Independence Measure (FIM)-cognition subscale. Linear and logistic regression analyses were conducted to evaluate if the difference between TUG and 10MWT was associated with cognitive functioning. Results: After adjusting for covariates, there was no significant linear association between TUG-10MWT discrepancy and cognition; however, stroke patients with the largest difference between TUG and 10MWT (highest quartile of scores) exhibited significantly worse attention on the TMT-A (adjusted odds ratio = 2.46, p = 0.04). Conclusions: A large difference between TUG and 10MWT may reflect deficits in complex sustained attention in individuals with stroke. Physical therapy staff may use this difference score to identify patients with potential cognitive deficits and refer them for comprehensive neuropsychological evaluation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".