Determinants of cognitive outcomes of perinatal and childhood stroke: A review
Bibliographic record
Abstract
Our understanding of cognitive and behavioral outcomes of perinatal and childhood stroke is rapidly evolving. A current understanding of cognitive outcomes following pediatric stroke can inform prognosis and direct interventions and our understanding of plasticity in the developing brain. However, our understanding of these outcomes has been hampered by the notable heterogeneity that exists amongst the pediatric stroke population, as the influences of various demographic, cognitive, neurological, etiological, and psychosocial variables preclude broad generalizations about outcomes in any one cognitive domain. We therefore aimed to conduct a detailed overview of the published literature regarding the effects of age at stroke, time since stroke, sex, etiology, lesion characteristics (i.e., location, laterality, volume), neurologic impairment, and seizures on cognitive outcomes following pediatric stroke. A key theme arising from this review is the importance of interactive effects among variables on cognitive outcomes following pediatric stroke. Interactions particularly of note include the following: (a) age at Stroke x Lesion Location; (b) Lesion Characteristics (i.e., volume, location) x Neurologic Impairment; (c) Lesion Volume x Time Since Stroke; (d) Sex x Lesion Laterality; and (e) Seizures x Time Since Stroke. Further, it appears that these relationships do not always apply uniformly across cognitive domains but, rather, are contingent upon the cognitive ability in question. Implications for future research directions are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".