Language disturbances after non-thalamic subcortical stroke: a review of the literature
Bibliographic record
Abstract
Language disorders following subcortical non-thalamic stroke show great variability across literature and a well-defined profile in these aphasic disturbances is yet to be established. The lack of recent and consistent literature on the subject complicates the management of subcortical aphasia. The aim of this study is to review the literature describing oral language disturbances following subcortical non-thalamic stoke affecting the basal ganglia and the surrounding white matter. A review of the literature of three databases (PubMed, PsycNet and LLBA), identifying research articles from 1997 to 2015, was completed. The quality of the selected studies was assessed using the Checklist for the assessment of methodological quality. Twenty-two articles met criteria for review and oral language assessment data were extracted for 114 subjects. The results suggest a predominance of deficits in more complex and demanding language levels (ex. discourse, syntax) and in language production (vs comprehension). Rapid recovery is expected, especially for lexical-semantic and receptive deficits. These findings show the importance of a complete oral language evaluation after subcortical stoke and provide recent data relative to expected deficits and recovery to guide clinicians in the management of these patients. They also suggest that a descriptive approach of the deficits may be more efficient and accurate than the use of a traditional classification of aphasia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".