Aphasia Owing to Subcortical Brain Infarcts in Childhood
Bibliographic record
Abstract
The aim of this study was to further define the clinical features of subcortical aphasia in children with deep brain infarcts and to define the sequelae associated with childhood strokes. We retrospectively studied nine children with left subcortical brain infarcts who presented with acquired language disorder and underwent language investigations based on standardized tests. Stroke in these patients involved the left internal capsule, lenticular or thalamic nuclei, or a combination of these. Early aphasic manifestations following the deep cerebral infarcts affected language expression. These included mutism, nonfluent speech, word finding difficulties, and phonemic and semantic paraphasia. Speech comprehension was generally more preserved. All patients subsequently improved, although variably; sequelae such as dysfluency, word finding difficulties, and written language learning impairment could be detected through standardized tests in six of them (all younger than 6 years at the time of the infarct). Two of the three remaining patients (both older than 6 years at the time of the infarct) had a full recovery. Our study confirms the concept of childhood subcortical aphasia, depicts the linguistic profile in these patients, and sustains the indication of systematic formal language assessment during the follow-up of all children with subcortical infarct involving the dominant hemisphere.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".