The effects of body position and actual execution on motor imagery of locomotor tasks in people with a lower-limb amputation
Bibliographic record
Abstract
Motor imagery (MI) is usually facilitated when performed in a congruent body position to the imagined movement, as well as after actual execution (AE). A lower-limb amputation (LLA) results in important structural and functional changes in the sensorimotor system, which can alter MI. In this study, we investigated the effects of body position and AE on the temporal characteristics of MI in people with LLA. Ten participants with LLA (mean age = 59.6 ± 13.9 years, four females) and ten gender- and age-matched healthy control participants (mean age = 60.1 ± 15.4 years, four females) were included. They performed two locomotor-related tasks (a walking task and the Timed Up and Go task) while MI times were measured in different conditions (in congruent/incongruent positions and before/after AE). We showed that MI times were significantly shorter when participants imagined walking in a congruent-standing position compared to an incongruent-sitting position, and when performing MI after actual walking compared to before, in both groups. Shorter MI times in the congruent position and after AE suggest an improvement of MI's temporal accuracy (i.e. the ability to match AE time during MI) in healthy individuals but not in the LLA group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".