The COMbined Physical and somatoSEnsory (COMPoSE) training intervention to improve upper limb recovery after stroke: a single-case experimental study
Bibliographic record
Abstract
PURPOSE: The COMbined Physical and somatoSEnsory (COMPoSE) program is a novel intervention combining training of somatosensory and motor variables synchronously to improve upper limb recovery after stroke. The aim of this study was to evaluate the impact of COMPoSE on the upper limbs after stroke, using a single-case experimental study design. METHODS: Five people with chronic stroke (62-89 years) completed the COMPoSE intervention trial (15 h, 10 sessions). Effects on participants were assessed using laboratory measures (maximal tactile pressures) and clinical motor and somatosensory measures. RESULTS: Notable improvements were observed in measures of maximal tactile pressures in four out of five participants between baseline and post-intervention (range of change index: 12.0-62.5%; change in level: 2.3-10.6 KPa). Also, improvements were observed in the Wolf motor function test (score and time), box and block test, motor activity log, grip strength, wrist position sense test, tactile discrimination test, stroke impact scale at post-intervention (range of change index: 3.0-50.3%) compared to baseline. CONCLUSION: Our findings suggest that COMPoSE could be beneficial to people with mild to severe somatosensory and motor deficits after stroke. The delivery of the COMPoSE intervention could be tailored to individual needs to maximize somatosensory and motor improvements in the upper limb after stroke. CLINICAL TRIALS REGISTRY: This study was registered with the Australian New Zealand Clinical Trials Registry ACTRN12615001222538.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".