Predictors of motor outcome after childhood arterial ischemic stroke
Bibliographic record
Abstract
AIM: To identify clinical and radiological predictors of long-term motor outcome after childhood-onset arterial ischemic stroke (AIS) in the middle cerebral artery (MCA) territory. METHOD: Medical records of 69 children (36 females, 33 males; median age at index AIS 3y 3mo, range: 1mo-16y) who presented to Great Ormond Street Hospital with first AIS in the MCA territory were reviewed retrospectively. Cases were categorized using the Childhood AIS Standardized Classification and Diagnostic Evaluation (CASCADE). Magnetic resonance imaging (MRI) and angiography were evaluated. An Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was calculated on MRI. The Recurrence and Recovery Questionnaire assessed motor outcome and was dichotomized into good/poor. RESULTS: Eventual motor outcome was good in 49 children and poor in 20. There were no acute radiological predictors of eventual motor outcome. At follow-up, CASCADE 3A (i.e. moyamoya) and Wallerian degeneration were significantly associated with poor motor outcome. In the multivariate analysis, younger age and CASCADE 3A predicted poor motor outcome. INTERPRETATION: In the context of recommendations regarding unproven and potentially high-risk hyperacute therapies for childhood AIS, prediction of outcome could usefully contribute to risk/benefit analysis. Unfortunately, paradigms used in adults, such as ASPECTS, are not useful in children in the acute/early subacute phase of AIS. What this paper adds Adult paradigms, such as the Alberta Stroke Program Early Computed Tomography Score system, are not useful for predicting outcome in children. Younger children tend to have a poorer long-term prognosis than older children. Moyamoya is associated with poor prognosis.
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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 | 0.000 | 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 | 0.000 | 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".