Effect of Trunk Restraint on the Recovery of Reaching Movements in Hemiparetic Patients
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reaching movements made with the affected arm in hemiparetic patients are often accompanied by compensatory trunk or shoulder girdle movements, which extend the reach of the arm. We investigated the effects of the suppression of these compensatory movements on reaching ability in hemiparetic individuals. METHODS: Eleven healthy and 11 hemiparetic individuals participated. Three-dimensional kinematic analysis was used to quantify reaches made to a close and a distant target (near the limit of arm's length). Unrestrained reaches were compared with those in which shoulder girdle and trunk movements were restrained by a harness. RESULTS: During unrestrained reaching, abnormal trunk recruitment and limitations in elbow and shoulder movements were correlated with the degree of clinical stroke severity (r=-0.91 to -0.96) in hemiparetic patients. During trunk restraint, ranges of elbow and shoulder joint movement increased in both groups. In addition, elbow and shoulder interjoint coordination improved. This was caused by increases in the range of joint motion as well as by a better dynamic temporal relation between joints. CONCLUSIONS: Trunk restraint allowed patients with hemiparetic stroke to make use of arm joint ranges that are present but not normally recruited during unrestrained arm-reaching tasks. Thus, the underlying "normal" patterns of movement coordination may not be entirely lost after stroke. Appropriate treatments, such as trunk restraint, may be effective in uncovering latent movement patterns to maximize arm recovery in hemiparetic patients.
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 | 0.001 |
| 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".