Comparing Interventions Used in Randomized Controlled Trials of Upper Extremity Motor Rehabilitation Post-stroke in High-Income Countries and Low-to-Middle-Income Countries
Bibliographic record
Abstract
OBJECTIVE: To identify and compare interventions for upper extremity (UE) motor recovery poststroke in randomized controlled trials (RCTs) conducted in high-income countries (HICs) and low-to-middle-income countries (LMICs). DATA SOURCE: Systematic searches were conducted for RCTs published in English in 5 databases (CINAHL, Embase, PubMed, Scopus, and Web of Science) up to April 2021, in line with PRISMA guidelines. STUDY SELECTION: RCTs, including crossover design, were included if they were in English and evaluated an intervention for poststroke UE motor rehabilitation, in an adult population (≥18y) diagnosed with stroke. DATA EXTRACTION: Data on country of origin and type of intervention in each RCT were extracted using a data extraction template in Covidence software. Study screenings and data extraction were performed by 2 independent reviewers. DATA SYNTHESIS: A total of 1276 RCTs met the inclusion criteria, with 978 RCTs conducted in HICs and 298 in LMICs. A significantly larger proportion of RCTs evaluating robotics and task-specific training interventions were conducted in HICs, compared to LMICs (P<.009). In contrast, a higher proportion of RCTs conducted in LMICs examined acupuncture (P<.001) and repetitive transcranial magnetic stimulation (rTMS) (P=.001) when compared to HICs. CONCLUSIONS: Poststroke rehabilitation in LMICs is conducted in a lower resource environment when compared to HICs. Some differences exist in the use of UE motor rehabilitation interventions between LMICs and HICs such as robotics, task-specific training, rTMS, and acupuncture; however, there was no significant difference between HICs and LMICs for most interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.223 | 0.529 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.022 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".