Pediatric Stroke Impairs Theory of Mind Performance
Bibliographic record
Abstract
AIM: This pilot study explored whether childhood stroke impairs performance on theory of mind (ToM) tasks and whether ToM task performance correlates with resting state connectivity in brain regions linked with social cognition. METHOD: We performed a case-control study of 10 children with stroke and 10 age- and gender-matched controls. They completed 2 ToM tasks, and resting state connectivity was measured with functional magnetic resonance imaging (MRI). RESULTS: Children with stroke performed worse than controls on conative ToM tasks. Resting state connectivity in the central executive network was significantly higher and connectivity between right and left inferior parietal lobules was significantly decreased in children with stroke. Resting state activity and ToM performance were not significantly correlated. INTERPRETATION: Childhood stroke results in poorer performance on specific ToM tasks. Stroke is associated with changes in resting state connectivity in networks linked with social cognition including ToM. Although the basis for these changes in connectivity is not well understood, these results may provide preliminary insights into potential mechanisms affecting social cognition after stroke. The findings suggest that further study of the effect of childhood stroke on network connectivity may yield insights as to how stroke affects cognitive functions in children.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".