A systematic review of randomized controlled trial characteristics for interventions to improve upper extremity motor recovery post stroke
Bibliographic record
Abstract
Given the prevalence of motor deficits post-stroke, a large proportion of stroke rehabilitation interventions are directed toward motor recovery. The purpose of this study is to present a detailed investigation of the methodological characteristics in the stroke rehabilitation literature with respect to randomized controlled trials (RCTs) designed to facilitate upper extremity motor recovery. This review was conducted following guidelines from the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) statement. English articles of RCTs were eligible if they were published before April 1, 2021 and applied an intervention to the hemiparetic upper extremity of individuals post stroke as the primary objective of the study, or recorded at least one upper extremity related outcome measure. The number of RCTs for upper extremity rehabilitation interventions post stroke has been increasing, with over three quarters of RCTs published in the last decade. In total, 1,307 RCTs met inclusion criteria for which the mean sample size (start/finish) was 45.8 (SD 55.4)/41.8 (SD 49.7). The median sample size (start/finish) was 30 (IQR 20–48)/29 (IQR 19–44). The mean PEDro score was 6.12 (SD 1.55). 251 RCTs (19%) were multi-centered trials. Key methodological measures of quality remain low including the blinding of assessors (59%), intention to treat analyses (42%) and concealed allocation (37%). There is a large number of RCTs evaluating stroke rehabilitation upper extremity interventions. Research quality continues to be a challenge (low percentage of key quality indicators, small percentage of multicentred trials, small sample sizes) but is slowly improving.
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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 | 0.079 | 0.316 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".