Effect of trunk-restraint training on function and compensatory trunk, shoulder and elbow patterns during post-stroke reach: a systematic review
Bibliographic record
Abstract
PURPOSE: The purpose of this systematic review was to determine the effect of trunk restraint (TR) training on post-stroke compensatory trunk movements during functional reach, and to identify functional gains of TR within the context of the International Classification of Functioning, Disability and Health (ICF). METHODS: Six databases were searched prior to 20 February 2013. Randomized control trials (RCTs) comparing the effects of a post-stroke reaching intervention with and without TR in the adult post-stroke population were included in this review. Data related to study inclusion/exclusion criteria, methodology, study participants and between-group outcomes (p < 0.05) were extracted. All outcomes were categorized according to functional domains defined within the ICF. RESULTS: Five RCTs met the inclusion criteria. TR groups demonstrated significant between-group differences (p < 0.05) for decreased compensatory trunk displacement (4/5 RCTs), increased elbow extension (3/5 RCTs) and increased shoulder flexion (2/5 RCTs). All significant between-group differences fell within the Body Structure/Function domain of the ICF. CONCLUSIONS: Trunk restraint is a simple, cost-effective technique that may help to reduce compensatory trunk/shoulder/elbow movements in the post-stroke adult population. Synthesis of study outcomes also highlights applications of TR to clinical practice and areas for further research. Implications for Rehabilitation The ability to use the shoulder and elbow to perform functional reach is a primary goal in post-stroke recovery; however, compensatory trunk movements are often used to achieve the reaching goal. Long-term use of compensatory strategies may contribute to secondary impairments, such as learned non-use, joint contractures and pain. Trunk restraint enables functional reach practice, while limiting compensatory strategies in the moderately to severely impaired stroke population.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".