Investigating the dose‐response relationship between motor imagery and motor recovery of upper‐limb impairment and function in chronic stroke: A scoping review
Bibliographic record
Abstract
The recovery of upper-limb impairment and dysfunction post-stroke is often incomplete owing to the limited time in therapy focused on upper-limb recovery and the severity of the impairment. In these cases, motor imagery (MI) may be used as a precursor to physical therapies to initiate rehabilitation early on when it would be otherwise impossible to engage in therapy, as well as to increase the dose of therapy when MI is used in adjunct to physical therapy. While previous reviews have shown MI to be effective as a therapeutic option, disparity in findings exists, with some studies suggesting MI is not an effective treatment for post-stroke impairment and dysfunction. One factor contributing to these findings is inconsistency in the dose of MI applied. To explore the relationship between MI dose and recovery, a scoping review of MI literature as a treatment for adult survivors of stroke with chronic upper-limb motor deficit was performed. Embase, Medline and CINHAL databases were searched for articles related to MI and stroke. Following a two-phase review process, 21 papers were included, and data related to treatment dose and measures of impairment and function were extracted. Effect sizes were calculated to investigate the effect of dosage on motor recovery. Findings showed a high degree of variability in dosage regimens across studies, with no clear pattern for the effect of dose on outcome. The present review highlights the gaps in MI literature, including variables that contribute to the dose-response relationship, that future studies should consider when implementing MI.
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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 | 0.025 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".