Comparing the Characteristics of Randomized Controlled Trials of Poststroke Upper Extremity Rehabilitation in Low-Middle-Income and High-Income Countries
Bibliographic record
Abstract
ABSTRACT: This review aimed to systematically identify and compare randomized controlled trials of poststroke upper extremity rehabilitation interventions conducted in low-middle-income countries and high-income countries over time and their differences in study characteristics and quality. Searches were conducted in CINAHL, Embase, PubMed, Scopus, and Web of Science up to April 1, 2021. Randomized controlled trials were included if ≥50% of the study population had stroke, if participants were adults (≥18 yrs), and if the randomized controlled trial examined an intervention to the hemiparetic upper extremity. A total of 1276 randomized controlled trials met inclusion criteria, and of these, 978 randomized controlled trials were conducted in high-income countries and 298 in low-middle-income countries. The number of randomized controlled trials increased at a comparable rate to high-income countries since 2011 although from a lower baseline. A higher percentage of randomized controlled trials in high-income countries were conducted in the chronic poststroke phase, and a higher percentage of randomized controlled trials in low-middle-income countries were conducted in the subacute phase. While the randomized controlled trials in low-middle-income countries were found to have comparable quality to randomized controlled trials of high-income countries, they were published in aggregate in journals with lower impact factors. It is important to better understand the potential barriers to publication in higher impact journals for randomized controlled trials conducted in low-middle-income countries.
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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 | 0.237 | 0.621 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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; 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".