Characteristics of randomized controlled trials of lower extremity interventions for post-stroke recovery in low-to-middle-income countries and high-income countries
Bibliographic record
Abstract
BACKGROUND: A better understanding of the overall picture of post-stroke motor trials in relation to country resources can be an important step to further understand potential disparities. OBJECTIVE: To characterize randomized controlled trials (RCTs) of interventions for the rehabilitation of post-stroke lower extremity (LE) motor disorders, conducted in high-income countries (HICs) and in low-to-middle-income countries (LMICs). METHODS: Systematic searches of RCTs in English were conducted in MEDLINE, Embase, CINAHL and PsycINFO, up to December 2024, according to the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA). RESULTS: = 0.04) were more likely to be examined in LMICs. A higher percentage of RCTs in HICs (28%) were published in journals with an impact factor of >3, compared to LMICs (18.4%), despite similar quality indicators and larger sample sizes in LMICs. CONCLUSION: The number of RCTs from LMICs has surpassed HICs on an annual basis after 2022. RCTs from LMICs are more often published in journals with lower JIF, despite similar quality. Interventions studied were similar, challenging broad assumptions about LMICs evaluating less costly 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".