Efficacy of Noninvasive Brain Stimulation Techniques in Managing Stuttering Behaviors: A Systematic Review and Meta-Regression Analysis
Bibliographic record
Abstract
PURPOSE: This systematic review and meta-regression analysis investigated the overall effectiveness of noninvasive brain stimulation (NIBS) techniques in managing stuttering behaviors. METHOD: A total of 290 papers were initially identified through a comprehensive database search, and after applying inclusion and exclusion criteria, 15 studies were selected for the final analysis. These studies evaluated NIBS techniques both as standalone interventions and in combination with speech therapy techniques. The random-effects meta-analysis was done to investigate the effect of neuromodulation techniques on reducing severity and frequency of stuttering behaviors. In addition, meta-regression and subgroup analyses were conducted to identify the effective techniques and explore potential moderators, such as intervention type, age group, and outcome measures. RESULTS: The random-effects meta-analysis revealed a significant positive effect of neuromodulation techniques on reducing stuttering severity and frequency. Meta-regression showed that transcranial direct current stimulation (tDCS) had the most significant effect in reducing stuttering severity and frequency among standalone interventions. Combined therapy approaches, which paired NIBS with speech therapy, resulted in the most substantial improvements overall. Sensitivity analyses confirmed the robustness of the results despite minor heterogeneity across studies. CONCLUSIONS: NIBS, particularly tDCS, shows promise as an effective intervention for stuttering. When combined with behavioral therapies, NIBS offers enhanced benefits, supporting its role as an adjunctive treatment in clinical practice. Further large-scale studies are recommended to confirm the long-term efficacy, refine treatment protocols, and explore optimal stimulation parameters for improved outcomes.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.063 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".