A Systematic Review and Meta‐Analysis on the Use of Repetitive Transcranial Magnetic Stimulation for Spasticity Poststroke
Bibliographic record
Abstract
BACKGROUND: Spasticity is a common and potentially debilitating complication that develops after stroke, arising in approximately 30% of patients. OBJECTIVE: To evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) in improving spasticity after stroke. DESIGN: Meta-analysis and systematic review. SETTING: Not applicable. PATIENTS: A total of 273 poststroke (hemorrhagic = 123, ischemic = 150) participants were included with sample sizes ranging from 5 to 80. The majority of participants were male (66.0%) with a mean age ranging from 55.0 to 64.6 years. Mean stroke duration ranged from 6 months to 10 years. METHODS: A literature search of multiple databases was conducted for articles published in English from January 1980 to April 2015 using select keywords. Studies were included if (1) the population included was >50% stroke patients; (2) the sample size included ≥4 subjects; (3) the intervention applied was rTMS; and (4) upper extremity spasticity was assessed pre- and postintervention. Randomized controlled trials (RCTs) were assessed for methodologic quality with the Physiotherapy Evidence Database tool. All research designs were given a level of evidence according to a modified Sackett Scale. MAIN OUTCOME MEASUREMENTS: Modified Ashworth Scale (MAS). RESULTS: Ten studies met the inclusion criteria: 2 RCTs (Physiotherapy Evidence Database scores 8-9) and 8 pre-post studies. Meta-analyses of primarily uncontrolled pre-post studies found significant improvements in MAS for elbow (P < .001), wrist (P < .001), and finger flexors (P < .001). However, a meta-analysis of the 2 available RCTs failed to find a significant rTMS treatment effect on MAS for the wrist (standardized difference = .34, P = .30). CONCLUSIONS: There is limited available evidence to support the use of rTMS in improving spasticity poststroke. Despite the positive findings reported, better powered and appropriately controlled trials are necessary. LEVEL OF EVIDENCE: II.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 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".