Pediatric ASPECTS predicts outcomes following acute symptomatic neonatal arterial stroke
Bibliographic record
Abstract
<h3>Objective</h3> To test the hypothesis that the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is useful in determining outcomes after neonatal arterial ischemic stroke (NAIS), we assessed accuracy of the modified pediatric ASPECTS (pedASPECTS) to predict cerebral palsy (CP), neurologic impairment, and epilepsy. <h3>Methods</h3> Cross-sectional study included newborns with acute NAIS whose outcomes were assessed at ≥18 months after stroke. PedASPECTS accuracy to predict outcomes was determined by sensitivity, specificity, and receiver operator characteristic (ROC) curves, and correlation between pedASPECTS and infarct volume was determined by the Spearman correlation coefficient. <h3>Results</h3> Ninety-six children met the inclusion criteria. Median percentage infarct to supratentorial brain volume was 6.8% (interquartile range [IQR] 3.0%–14.3%). Median pedASPECTS was 7 (IQR 4–10). At a median age of 2.1 years, 35% developed CP, 43% had neurologic impairment, and 7% had epilepsy. Median pedASPECTS predicted outcomes of interest: CP (10, IQR 8–12) vs no CP (5, IQR 4–8) (<i>p</i> < 0.0001), poor (9, IQR 7–12) vs good (6, IQR 4–8) neurologic outcomes (<i>p</i> < 0.0001), and epilepsy (10, IQR 8–12) vs no epilepsy (7, IQR 4–10) (<i>p</i> = 0.033). PedASPECTS accuracy was good for CP (ROC 0.811) and fair for neurologic impairment (ROC 0.760) and epilepsy (ROC 0.761). A pedASPECTS ≥8 had ≥69% sensitivity and ≥54% specificity for clinical outcomes. PedASPECTS correlated with infarct volume (Spearman rank 0.701, <i>p</i> < 0.0001). <h3>Conclusions</h3> This study provides Class II evidence that pedASPECTS has fair to good accuracy for predicting CP, neurologic impairment, and epilepsy after NAIS and correlates with infarct volume. PedASPECTS may assist with early identification of babies requiring close developmental surveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".