Short-Term Effects of Practice With Trunk Restraint on Reaching Movements in Patients With Chronic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In prehension tasks with objects placed within arm's reach, patients with hemiparesis caused by stroke use excessive trunk movement to compensate for arm motor impairments. Compensatory trunk movements may improve motor function in the short term but may limit arm recovery in the long term. Previous studies showed that restriction of trunk movements during reach-to-grasp movements results in immediate increases in active arm joint ranges and improvement in interjoint coordination. To evaluate the potential of this technique as a therapeutic intervention, we compared the effects of short-term reach-to-grasp training (60-trial training session) with and without physical trunk restraint on arm movement patterns in patients with chronic hemiparesis. METHODS: A total of 28 patients with hemiparesis were assigned to 2 groups: 1 group practiced reach-to-grasp movements during which compensatory movement of the trunk was prevented by a harness (trunk restraint), and the second group practiced the same task while verbally instructed not to move the trunk (control). Kinematics of reaching and grasping an object placed within arm's length were recorded before, immediately after, and 24 hours after training. RESULTS: The trunk restraint group used more elbow extension, less anterior trunk displacement, and had better interjoint coordination than the control group after training, and range of motion was maintained 24 hours later in only the trunk restraint group. CONCLUSIONS: Restriction of compensatory trunk movements during practice may lead to greater improvements in reach-to-grasp movements in patients with chronic stroke than practice alone, and longer-term effects of this intervention should be evaluated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".