Predictors of neurocognitive outcome in pediatric ischemic and hemorrhagic stroke
Bibliographic record
Abstract
This clinical study examined the impact of eight predictors (age at stroke, stroke type, lesion size, lesion location, time since stroke, neurologic severity, seizures post-stroke, and socioeconomic status) on neurocognitive functioning following pediatric stroke. Youth with a history of pediatric ischemic or hemorrhagic stroke (n = 92, ages six to 25) underwent neuropsychological testing and caregivers completed parent-report questionnaires. Hospital records were accessed for medical history. Spline regressions, likelihood ratios, one-way analysis of variance, Welch’s t-tests, and simple linear regressions examined associations between predictors and neuropsychological outcome measures. Large lesions and lower socioeconomic status were associated with worse neurocognitive outcomes across most neurocognitive domains. Ischemic stroke was associated with worse outcome in attention and executive functioning compared to hemorrhagic stroke. Participants with seizures had more severe executive functioning impairments than participants without seizures. Youth with cortical-subcortical lesions scored lower on a few measures than youth with cortical or subcortical lesions. Neurologic severity predicted scores on few measures. No differences were found based on time since stroke, lesion laterality, or supra- versus infratentorial lesion. In conclusion, lesion size and socioeconomic status predict neurocognitive outcome following pediatric stroke. An improved understanding of predictors is valuable to clinicians who have responsibilities related to neuropsychological assessment and treatments for this population. Findings should inform clinical practice through enhanced appraisals of prognosis and the use of a biopsychosocial approach when conceptualizing neurocognitive outcome and setting up support services aimed at fostering optimal development for youth with stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".