Task-Specific Training With Trunk Restraint on Arm Recovery in Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Task-specific training improves functional outcomes after stroke. However, gains may be accompanied by increases in movements compensating for motor impairments. We hypothesized that restriction of compensatory trunk movements may encourage recovery of premorbid movement patterns leading to better functional outcomes. The goal was to determine whether task-specific training with trunk-restraint (TR) produces greater improvements in arm impairment and function than training without TR in patients with chronic hemiparesis. METHODS: Double-blind randomized control trial of a therapist-supervised home program (3 times per week, 5 weeks) in 30 patients with chronic hemiparesis stratified by arm impairment level (Fugl-Meyer) was performed. Intervention group (TR group) received progressive object-related reach-to-grasp training with prevention of trunk movements. Control group (C) practiced tasks without TR. Main outcome measures were upper limb impairment (Fugl-Meyer Arm Section) and function (TEMPA) and movement kinematics (trunk displacement, elbow extension; Optotrak, 10 trials) of a reach-to-grasp movement. Evaluations were repeated before, immediately after, and 1 month postintervention by blind evaluators. RESULTS: TR training led to greater improvements in impairment and function compared with C. Improvements were accompanied by increased active joint range and were greater in initially more severe patients. In these patients, TR decreased trunk movement and increased elbow extension, whereas C had opposite effects (increased compensatory movements). In TR, changes in arm function were correlated with changes in arm and trunk kinematics. CONCLUSIONS: Treatment should be tailored to arm impairment severity with particular attention to controlling excessive trunk movements if the goal is to improve arm movement quality and function.
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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".