Academic Outcome in Pediatric Stroke: A Multifaceted Approach to Exploring Challenges and Achievements
Bibliographic record
Abstract
Abstract Objective An important cause of acquired brain injury in children, pediatric stroke can engender sequelae across a wide range of cognitive domains, including language, attention, memory, and processing speed. As a result, survivors are especially vulnerable to academic difficulties and face unique challenges compared to their peers. Despite this knowledge, pediatric stroke remains an understudied neurological condition, and its impact on school functioning is poorly understood. Addressing this gap in knowledge, the present clinical research study assesses academic outcome with a multifaceted approach. Methods This study evaluated youth with (n = 23) and without (n = 18) histories of stroke. Families were recruited at the Hospital for Sick Children in Toronto. Participants were administered an extensive battery of neuropsychological and psychoeducational tests, quality of life questionnaires, and behavior rating scales. Medical records and school report cards provided additional data. Results Compared to their peers, youth with stroke exhibited more deficits in processing speed and core academic skills and were more likely to have a learning disability, an Individual Education Plan, school accommodations, and access to assistive technologies. Controlling for the effects of intelligence, a hierarchical regression suggested that processing speed and reading skills predicted grades for youth with stroke. Finally, quality of life was similar between groups, indicating comparable experiences in school environment, peer support, and social acceptance. Conclusions This study makes a meaningful contribution to the field of pediatric stroke and promotes a nuanced understanding of the struggles that patients encounter, what impairments they tend to incur, and how these difficulties impact academic achievement.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".