Predicting Recovery and Outcome after Pediatric Stroke: Results from the International Pediatric Stroke Study
Bibliographic record
Abstract
OBJECTIVE: To characterize predictors of recovery and outcome following pediatric arterial ischemic stroke, hypothesizing that age influences recovery after stroke. METHODS: We studied children enrolled in the International Pediatric Stroke Study between January 1, 2003 and July 31, 2014 with 2-year follow-up after arterial ischemic stroke. Outcomes were defined at discharge by clinician grading and at 2 years by the Pediatric Stroke Outcome Measure. Demographic, clinical, and radiologic outcome predictors were examined. We defined changes in outcome from discharge to 2 years as recovery (improved outcome), emerging deficit (worse outcome), or no change. RESULTS: Our population consisted of 587 patients, including 174 with neonatal stroke and 413 with childhood stroke, with recurrent stroke in 8.2% of childhood patients. Moderate to severe neurological impairment was present in 9.4% of neonates versus 48.8% of children at discharge compared to 8.0% versus 24.7% after 2 years. Predictors of poor outcome included age between 28 days and 1 year (compared to neonates, odds ratio [OR] = 3.58, p < 0.05), underlying chronic disorder (OR = 2.23, p < 0.05), and involvement of both small and large vascular territories (OR = 2.84, p < 0.05). Recovery patterns differed, with emerging deficits more common in children <1 year of age (p < 0.05). INTERPRETATION: Outcomes after pediatric stroke are generally favorable, but moderate to severe neurological impairments are still common. Age between 28 days and 1 year appears to be a particularly vulnerable period. Understanding the timing and predictors of recovery will allow us to better counsel families and target therapies to improve outcomes after pediatric stroke. ANN NEUROL 2020;87:840-852.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".