Utilization of motor imagery in upper limb rehabilitation: a systematic scoping review
Bibliographic record
Abstract
OBJECTIVE: To determine how motor imagery is being delivered in upper limb rehabilitation to guide practice and research. DATA SOURCE: MEDLINE, PubMed, CINAHL, EMBASE, PsychINFO databases were searched from 1987 to November 2014 STUDY SELECTION: English, adults, any clinical population or diagnosis, intervention for upper limb with an outcome measure used. All types of studies were included. Two authors independently selected studies for review using consensus. DATA EXTRACTION: Seven motor imagery elements were extracted using a model implemented in sport research: PETTLEP model (Physical, Environment, Task, Timing, Learning, Emotion, and Perspective). RESULTS: The search yielded 1107 articles with 1059 excluded leaving 48 articles for full review. A total of 38 articles involved individuals with stroke, five articles involved individuals with complex regional pain syndrome, and five articles for other conditions. Motor imagery elements most commonly described were physical, environment, task, and perspective. Elements less commonly described were timing, learning, and emotional aspects. There were significant differences between study populations (e.g. stroke and complex regional pain syndrome) and within populations on how motor imagery was delivered. CONCLUSION: Many of the imagery elements reviewed are not being considered or reported on in the selected studies. How motor imagery is being delivered within and between populations is inconsistent, which may lead to difficulties in determining key elements of effectiveness.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".