Cognitive Impairment Is Reflected by an Increased Difference between Real and Imagined Timed Up and Go Test Performance
Bibliographic record
Abstract
BACKGROUND: Recent research suggests using an imaginary version of the Timed Up and Go test (TUG) for a first assessment of cognitive impairment. By using the time difference between a real (TUGr) and an imagined (TUGi) TUG task, the objective of this study was to examine the effect of cognitive impairment on motor imagery ability. METHODS: Fifty-two participants (mean age 69.3 ± 4.0 years) with mild cognitive impairment or subjective cognitive impairment were included in this study. The time difference between the TUGr and the TUGi was used as the main outcome. The Trail Making Test part B (TMT B), the ratio between TMT A and TMT B, and the Montreal Cognitive Assessment (MoCA) battery were the main independent variables. RESULTS: The difference between TUGr and TUGi performance time and the TMT B performance time increased with decreasing cognitive function (p < 0.01). There was no relationship between TUGr and TUGi performance time and TMT B/A ratio. There were significant correlations between TUG time differences and the MoCA score (r = -0.489, p < 0.01), the TMT B (r = 0.364, p < 0.01), and the TMT B/A ratio (r = 0.377, p < 0.01). CONCLUSION: The combination of TUGr and TUGi may have added value in assessing cognitive impairment, which is a possible pre-stage of dementia.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".