Clinical Assessment of Motor Imagery After Stroke
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate: (1) the effects of a stroke on motor imagery vividness as measured by the Kinesthetic and Visual Imagery Questionnaire (KVIQ-20); (2) the influence of the lesion side; and (3) the symmetry of motor imagery. METHODS: Thirty-two persons who had sustained a stroke, in the right (n = 19) or left (n = 13) cerebral hemisphere, and 32 age-matched healthy persons participated. The KVIQ-20 assesses on a 5-point ordinal scale the clarity of the image (visual scale) and the intensity of the sensations (kinesthetic scale) that the subjects are able to imagine from the first-person perspective. RESULTS: In both groups, the visual scores were higher (P = .0001) than the kinesthetic scores and there was no group difference. Likewise, visual scores remained higher than kinesthetic scores irrespective of the lesion side. The visual scores poststroke were higher (P = .001) when imagining upper limb movements on the unaffected side than those on the affected side. When focusing on the lower limb only, however, the kinesthetic scores were higher (P = .001) when imagining movements of the unaffected compared to those on the affected side. CONCLUSIONS: The vividness of motor imagery poststroke remains similar to that of age-matched healthy persons and is not affected by the side of the lesion. However, after stroke motor imagery is not symmetrical and motor imagery vividness is better when imagining movements on the unaffected than on the affected side, indicating an overestimation possibly related to a hemispheric imbalance or a recalibration of motor imagery perception.
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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) | 0.000 | 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 | 0.000 |
| 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".