Safety and efficacy of recovery-promoting drugs for motor function after stroke: A systematic review of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy and safety of drug interventions to promote motor recovery post-stroke. DATA SOURCES: CENTRAL, CINAHL, Embase, MEDLINE, SCOPUS and Web of Science. STUDY SELECTION: Published human randomized controlled trials in which the primary intervention was a drug administered to promote motor recovery post-stroke, vs placebo. DATA EXTRACTION: Standardized pro forma used to extract safety and efficacy data; Cochrane Collaboration risk of bias assessment tool performed to assess risk of bias. DATA SYNTHESIS: Fifty randomized controlled trials from 4,779 citations were included. An overall trend of high risk of attrition (n = 27) and reporting bias (n = 36) was observed. Twenty-eight different drug interventions were investigated, 18 of which demonstrated statistically significant results favouring increased motor recovery compared with control intervention. Forty-four studies measured safety; no major safety concerns were reported. CONCLUSION: Candidate drug interventions promoting motor recovery post-stroke were identified, specifically selective serotonin reuptake inhibitors and levodopa; however, the high risk of bias in many trials is concerning. Drugs to improve motor function remain an important area of enquiry. Future research must focus on establishing the right drug intervention to be administered at an optimal dose and time, combined with the most effective adjuvant physical therapy to drive stroke recovery.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".