1 Predictors of Neurocognitive Outcome in Pediatric Ischemic and Hemorrhagic Stroke
Bibliographic record
Abstract
Objective: Neurocognitive deficits commonly occur following pediatric stroke and can impact many neuropsychological domains. Despite awareness of these deleterious effects, neurocognitive outcome after pediatric stroke, especially hemorrhagic stroke, is understudied. This clinical study aimed to elucidate the impact of eight factors identified in the scientific literature as possible predictors of neurocognitive outcome following pediatric stroke: age at stroke, stroke type (i.e., ischemic vs. hemorrhagic), lesion size, lesion location (i.e., brain region, structures impacted, and laterality), time since stroke, neurologic severity, seizures post-stroke, and socioeconomic status. Participants and Methods: Ninety-two patients, ages six to 25 and with a history of pediatric stroke, chose to participate in the study and were administered standardized neuropsychological tests assessing verbal reasoning, abstract reasoning, working memory, processing speed, attention, learning ability, long-term memory, and visuomotor integration. A standardized parent questionnaire provided an estimate of executive functioning. Statistical analyses included spline regressions to examine the impact of age at stroke and lesion size, further divided by stroke type; a series of one-way analysis of variance to examine differences in variables with three levels; Welch’s t-tests to examine dichotomous variables; and simple linear regressions for continuous variables. Results: Lesion size, stroke type, age at stroke, and socioeconomic status were identified as predictors of neurocognitive outcome in our sample. Large lesions were associated with worse neurocognitive outcomes compared to small to medium lesions across neurocognitive domains. Exploratory spline regressions suggested that ischemic stroke was associated with worse neurocognitive outcomes than hemorrhagic stroke. Based on patterns shown in graphs, age at stroke appeared to have an impact on outcome depending on the neurocognitive domain and stroke type, with U-shaped trends suggesting worse outcome across most domains when stroke occurred at approximately 5 to 10 years of age. Socioeconomic status positively predicted outcomes across most neurocognitive domains. Participants with seizures had more severe executive functioning impairments than youth without seizures. Youth with combined cortical-subcortical lesions scored lower on abstract reasoning than youth with cortical and youth with subcortical lesions, and lower on attention than youth with cortical lesions. Neurologic severity predicted scores on abstract reasoning, attention, processing speed, and visuomotor integration, depending on stroke type. There was no evidence of differences on outcome measures based on time since stroke, lesion laterality, or lesion region defined as supra-versus infratentorial. Conclusions: The current study contributed to the scientific literature by identifying lesion size, stroke type, age at stroke, and socioeconomic status as predictors of neurocognitive outcome following pediatric stroke. Future research should examine other possible predictors of neurocognitive outcome that remain unexplored. Multisite collaborations would provide larger sample sizes and allow teams to build models with better statistical power and more predictors. Enhancing understanding of neurocognitive outcomes following pediatric stroke is a first step towards improving appraisals of prognosis. Findings are clinically applicable as they provide professionals with information that can help assess individual expected patterns of recovery and thus refer patients to appropriate support services.
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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) | 0.002 | 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".