Occupational Therapy Treatment Associated with Graded Motor Imagery (GMI) for the Recovery of Hand Function in Patients with Acquired Brain Injuries: Outcome Research
Bibliographic record
Abstract
Objective: This study aims to evaluate the effectiveness of a combined rehabilitative program integrating Graded Motor Imagery (GMI) and Occupational Therapy in improving upper limb function and autonomy in individuals with acquired brain injuries (ABIs), including stroke and traumatic brain injury. Methods: Twelve patients (mean age of 56.4 years) underwent a six-week intervention combining GMI and Occupational Therapy. Outcome measures included the Disabilities of the Arm, Shoulder and Hand questionnaire (DASH), Jebsen Taylor Hand Function Test (JTHFT), Canadian Occupational Performance Measure (COPM), 12-Item Short Form Health Survey (SF-12), Numeric Rating Scale for pain (NRS), and Montreal Cognitive Assessment (MoCA). Assessments were conducted at baseline, post-treatment, and at 3- and 6-month follow-ups. Data were analyzed using the Wilcoxon signed-rank test. Results: Statistically significant improvements (p < 0.05) were found in upper limb function (DASH), occupational performance and satisfaction (COPM), and physical health status (SF-12 physical component). Specific gains in hand function—particularly in writing and eating—were detected using the JTHFT. No significant changes were observed in pain perception or mental health outcomes. Conclusions: The integration of GMI with Occupational Therapy appears to be a promising and well-tolerated intervention for enhancing motor function and daily life participation in individuals with ABI. Although the small sample limits generalizability, these preliminary findings support further investigation through larger, controlled studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".