Modified Constraint-Induced Movement Therapy for Upper Extremity Recovery Post Stroke: What Is the Evidence?
Bibliographic record
Abstract
BACKGROUND: Constraint-induced movement therapy (CIMT) is an effective treatment for upper extremity (UE) recovery post stroke. Difficulties implementing a traditional CIMT approach have led to development of protocols featuring varying practice schedules, including a 10-week, 3 times per week intervention, termed modified CIMT (mCIMT). To date, systematic reviews of CIMT have grouped the various protocols, precluding the ability to ascertain the level of evidence (LOE) of specific CIMT protocols. Knowing the LOE for various protocols and their relative effectiveness may facilitate decision making regarding which protocol to implement. OBJECTIVE: The aim of this study was to determine the LOE of mCIMT in promoting UE recovery post stroke. METHODS: A comprehensive literature search and subsequent analysis identified studies of a range of designs that investigated the mCIMT protocol. Two independent reviewers assigned an LOE to each of the identified studies, which were then examined collectively to determine the overall LOE for mCIMT. Study results were reviewed to assess the effectiveness of mCIMT for improving UE recovery. RESULTS: Of 473 studies identified, 15 utilized mCIMT. The lack of randomized controlled trials (RCT) resulted in assigning an intermediate LOE (C). Study results indicated that participants receiving mCIMT experienced clinically significant improvements in UE impairment and activity-level attributes. CONCLUSION: The mCIMT protocol is an effective intervention for UE recovery post stroke. Future research including large RCTs could potentially increase the LOE for mCIMT. Additional investigation into the effectiveness of mCIMT in acute and subacute stroke populations is warranted given the limited number of studies performed to date.
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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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".