Music‐based interventions for nonfluent aphasia: A systematic review of randomized control trials
Bibliographic record
Abstract
Music-based interventions (MBIs) offer promising strategies for addressing speech-language impairments in individuals with nonfluent aphasia. This systematic review summarizes the current literature of MBIs for nonfluent aphasia recovery by types of MBIs to determine the efficacy of MBIs and assesses the risk of bias to identify common methodological limitations. A systematic search was conducted of MEDLINE, PubMed, and APA PsycInfo for the 20 years preceding July 2024. Risk of bias assessment was performed using the revised Joanna Briggs Institute critical appraisal tool for randomized controlled trials (RCTs). Ten RCTs met the inclusion criteria, featuring MBIs such as Melodic Intonation Therapy, Modified Melodic Intonation Therapy, and singing-based approaches. The results highlighted the potential of MBIs in various domains, particularly in enhancing repetition and naming abilities, even when compared to speech therapy. The reviewed studies exhibited a moderate to high risk of bias. Outcome measures varied widely, and functional communication, a critical rehabilitation goal, was examined in just two RCTs. Furthermore, heterogeneous control conditions and statistical methods hindered meaningful comparisons across studies. Future research should prioritize functional communication outcomes and refine intervention protocols to strengthen the evidence base. Addressing these gaps is essential for advancing the potential benefits of these clinical tools for nonfluent aphasia 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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".