Therapeutic Interventions for Aphasia Initiated More than Six Months Post Stroke: A Review of the Evidence
Bibliographic record
Abstract
BACKGROUND: Aphasia effects up to 38% of acute stroke patients. For many of these individuals, this condition persists far beyond the acute phase. The purpose of this review is to evaluate the effectiveness of therapeutic interventions for aphasia initiated more than 6 months post stroke. METHODS: A literature search was conducted for articles in which aphasia treatments were initiated more than 6 months post stroke. Searches were conducted in multiple databases including MEDLINE, Scopus, CINAHL, and EMBASE. RESULTS: A total of 21 randomized controlled trials (RCTs) met the inclusion criteria. There is good evidence to suggest that the use of computer-based treatments, constraint-induced therapy, intensity of therapy, group language therapies, and training conversation/communication partners are effective treatments for chronic aphasia. Repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and the use of the drugs piracetam, donepezil, memantime, and galantamine have also demonstrated evidence that they are effective treatments of aphasia 6 months or more post stroke onset. Neither filmed language instruction nor the drug bromocriptine has been shown to be effective in treating chronic aphasia. CONCLUSIONS: There is evidence to support the use of a number of treatments for chronic aphasia post stroke. Further research is required to fully support the use of these interventions and to explore the effectiveness of other aphasia interventions in the chronic stage.
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 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| 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 | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".