Neurocognitive outcomes following intracerebral hemorrhage in childhood
Bibliographic record
Abstract
Neurocognitive deficits commonly occur following intracerebral hemorrhage (ICH) in childhood, yet this population remains understudied. The current study is a preliminary exploration of neurocognitive outcomes in this population. At the Hospital for Sick Children in Toronto, Canada, 17 patients (Mage = 14.2, SD = 4.6) with a history of childhood ICH completed a neuropsychological assessment evaluating perceptual reasoning, verbal reasoning, processing speed, working memory, verbal learning, verbal memory, visuomotor integration, selective attention, and executive functioning. Mean Full Scale IQ (FSIQ; M = 98.1, SD = 13.6) fell within the clinically average range compared to population norms, though it was skewed toward lower ranges. Furthermore, approximately 50–60% of the participants scored under the clinically average range on tests of verbal learning, verbal memory, processing speed, and visuomotor integration. Youth with childhood ICH may present with FSIQ within the average range, but as a group they skew toward lower ranges and are more likely to demonstrate deficits in distinct neurocognitive domains. Clinical evaluation of a wide range of neuropsychological skills is warranted. Clinical implications encompass informing of intake interviews, development of test batteries, and appraisal of prognosis. Findings contribute to the limited knowledge base about neurocognitive outcomes following childhood ICH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".