Pediatric ASPECTS predicts outcomes following acute symptomatic neonatal arterial stroke
Bibliographic record
Abstract
Objective 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. Methods 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. Results 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) (p < 0.0001), poor (9, IQR 7–12) vs good (6, IQR 4–8) neurologic outcomes (p < 0.0001), and epilepsy (10, IQR 8–12) vs no epilepsy (7, IQR 4–10) (p = 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, p < 0.0001). Conclusions 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".