The complementary role of the cerebral hemispheres in recovery from aphasia after stroke: A critical review of literature
Bibliographic record
Abstract
OBJECTIVES: To review the literature on the specific role of the right cerebral hemisphere during recovery from aphasia in order to address the lack of consensus among authors. To derive a theoretical model reconciling the controversial findings in the literature. METHODS: Initial PubMed, MEDLINE (1946 to 5 May 2012) and PsycINFO (1806 to first week June 2012) searches on recovery mechanisms from aphasia, whether treatment-related or not, retrieved a total of 35 English language articles. Articles, cross-referenced in this initial set were also reviewed if they met the inclusion criteria, thus resulting in a total of 42 articles included in this review. MAIN OUTCOMES: Recruitment of the right hemisphere during recovery from aphasia can be effective if it occurs during a critical time window post-stroke. The recruitment's effectiveness will depend on the lesion's location, extent and permanence. Preservation of core language processing areas will generate minimal right hemisphere recruitment and vice versa. Some experimental studies seem to suggest that the improvement linked to a particular hemisphere can be modulated by specific therapy methods. CONCLUSION: The specific conditions in which effective right recruitment takes place may have important implications for rehabilitation treatment. These findings could lead to improved recovery in people suffering from aphasia. However, more research with non-invasive brain stimulation is needed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".