Mortality After Pediatric Arterial Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVES: Cerebrovascular disease is among the top 10 causes of death in US children, but risk factors for mortality are poorly understood. Within an international registry, we identify predictors of in-hospital mortality after pediatric arterial ischemic stroke (AIS). METHODS: Neonates (0–28 days) and children (29 days–<19 years) with AIS were enrolled from January 2003 to July 2014 in a multinational stroke registry. Death during hospitalization and cause of death were ascertained from medical records. Logistic regression was used to analyze associations between risk factors and in-hospital mortality. RESULTS: Fourteen of 915 neonates (1.5%) and 70 of 2273 children (3.1%) died during hospitalization. Of 48 cases with reported causes of death, 31 (64.6%) were stroke-related, with remaining deaths attributed to medical disease. In multivariable analysis, congenital heart disease (odds ratio [OR]: 3.88; 95% confidence interval [CI]: 1.23–12.29; P = .021), posterior plus anterior circulation stroke (OR: 5.36; 95% CI: 1.70–16.85; P = .004), and stroke presentation without seizures (OR: 3.95; 95% CI: 1.26–12.37; P = .019) were associated with in-hospital mortality for neonates. Hispanic ethnicity (OR: 3.12; 95% CI: 1.56–6.24; P = .001), congenital heart disease (OR: 3.14; 95% CI: 1.75–5.61; P < .001), and posterior plus anterior circulation stroke (OR: 2.71; 95% CI: 1.40–5.25; P = .003) were associated with in-hospital mortality for children. CONCLUSIONS: In-hospital mortality occurred in 2.6% of pediatric AIS cases. Most deaths were attributable to stroke. Risk factors for in-hospital mortality included congenital heart disease and posterior plus anterior circulation stroke. Presentation without seizures and Hispanic ethnicity were also associated with mortality for neonates and children, respectively. Awareness and study of risk factors for mortality represent opportunities to increase survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".