Predictors of neurocognitive outcome following childhood hemorrhagic stroke in the left hemisphere: A case series
Bibliographic record
Abstract
Neurocognitive deficits commonly occur following hemorrhagic stroke in childhood, yet the understanding of recovery patterns and prognostic factors remains limited. Hematoma location, lateralization, volume, and age at injury have been identified as predictors of neurocognitive outcomes. This case review aims to describe and discuss the influence of these factors on neurocognitive outcomes following hemorrhagic stroke in three pediatric cases. Three patients (one female age 13, two males ages 15 and 17) with a history of childhood hemorrhage were selected from a larger cohort due to their similar etiology (i.e., arteriovenous malformation) and similar lesion location (i.e., broadly within the temporoparietal region). Participants completed a neuropsychological assessment evaluating verbal comprehension, perceptual reasoning, processing speed, working memory, verbal fluency, language, learning, memory, and executive functioning. Results suggest variable language outcomes despite similar clinical characteristics. Both Case 1 and Case 2, who had medium-sized hematomas, exhibited challenges with verbal learning, verbal memory, word finding, and word generation. In contrast, Case 3, who had a small-sized hematoma, showed broadly preserved verbal abilities. All three cases exhibited challenges on at least one measure of executive functioning. The distinct performance of the three cases highlights the complexity of predicting neurocognitive abilities following childhood left hemisphere hemorrhagic stroke. The finding that all cases exhibited executive functioning deficits suggests an area of vulnerability in this population. Clinical implications include the importance of close monitoring and follow-up through comprehensive neuropsychological assessment in this population.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".