Differences in stroke rehabilitation motor and cognitive randomized controlled trials by world region: Number, sample size, and methodological quality
Bibliographic record
Abstract
BACKGROUND: Stroke rehabilitation research is important for informing clinical practice and directing health care resources. OBJECTIVE: To examine how motor- and cognitive-based stroke rehabilitation randomized controlled trials (RCTs) vary by world region, overall and over time, with respect to 1) publication volume, 2) sample size, and 3) methodological quality. METHODS: Using the Evidence-Based Review of Stroke Rehabilitation (EBRSR), all motor- and cognitive-based stroke rehabilitation RCTs were identified. The following data were extracted: first author, year of publication, country of origin, and sample size. Countries were categorized into seven regions, as defined by the World Bank. RESULTS: In total 1410 motor-based RCTs and 293 cognitive-based RCTs were published between 1972-2018. For motor RCTs, the East Asia/Pacific region accounted for the largest volume of RCTs (n = 530; 37.6%), followed closely by the Europe/Central Asia region (n = 445; 31.6%). Conversely, the largest producer for cognitive RCTs was Europe/Central Asia (n = 167; 57.0%), followed by East Asia/Pacific (n = 62; 21.2%). For both motor and cognitive RCTs, there was no significant difference between world regions with respect to mean sample size or methodological quality. CONCLUSIONS: Efforts should be directed towards improving methodological quality and increasing sample sizes of stroke rehabilitation-related studies.
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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 | 0.480 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".