Time Post-Stroke and Upper Extremity Stroke Motor Recovery Rehabilitation: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Understanding the impact of timing of post-stroke motor recovery research trials is critical for clinical care. OBJECTIVE: To examine and compare differences in Fugl-Meyer Assessment Upper Extremity (FMA-UE) scores at 2 different time points post-stroke on the effectiveness of upper extremity (UE) rehabilitation interventions compared to conventional care or sham therapy controls in stroke rehabilitation randomized controlled trials (RCTs). METHODS: A meta-analysis was conducted in accordance with PRISMA guidelines. Searches were conducted in CINAHL, Embase, PubMed, Scopus, and Web of Science, up to April 1st, 2021. Inclusion criteria were: (1) English RCTs of adults (≥18 years) diagnosed with stroke; (2) examined a single intervention for stroke UE rehabilitation; (3) used conventional care/sham as the control arm; and (4) assessed FMA-UE as one of the outcome measures. RESULTS: 157 RCTs were included, including 17 types of interventions. In the acute and subacute phases post stroke, 16 interventions were assessed, and the analyses of 11 interventions showed significant beneficial effects. In the chronic post-stroke phase, 9 intervention types were assessed, and 7 of them showed significant improvements. Greater FMA-UE score improvements were found for the same interventions in the acute and subacute post-stroke phases when compared to the chronic phase. CONCLUSIONS: Interventions studied in the acute and subacute phases showed greater magnitude improvements in the FMA-UE scores compared to the chronic phase. The effectiveness of upper extremity rehabilitation interventions may be underestimated when studied exclusively in the chronic phase, with some of the observed differences potentially attributable to variations in baseline severity.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.057 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".