Neural mechanisms driving speech and language recovery following childhood stroke: a scoping review
Bibliographic record
Abstract
PURPOSE: This scoping review aimed to inform future research priorities by collating evidence on neural correlates of speech and language recovery following childhood stroke. METHODS: Neuroimaging, motor speech, or language outcomes following childhood stroke (28 days to 18 years age) in the subacute to chronic community stages (care occurring after acute medical management, including inpatient and outpatient rehabilitation, and community-based programs) were identified and extracted from Medline, Embase, PsycInfo, and Clinical databases. RESULTS: Of the 3990 studies screened, 11 met the inclusion criteria. Of the included articles, no papers formally assessed speech outcomes, 11 articles reported language outcomes through standardized testing, 11 utilized structural imaging (CT, MRI), and four reported functional neuroimaging outcomes (fMRI). INTERPRETATION: This review revealed a rudimentary accounting of speech and language profiles in children post-stroke; limited by the use of varied and incomplete speech and language assessment batteries, inconsistent reporting of lesion locations associated with speech and language outcomes, a dearth of functional neuroimaging studies, and lack of information about speech and language function throughout the rehabilitation period, a time when the brain is most plastic and receptive to therapy. Future research should provide complete and accurate accounts of speech and language function and their neural correlates throughout rehabilitation and recovery to inform care, education, and employment planning.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".