Abstract 15: Mortality After Pediatric Arterial Ischemic Stroke: Results From the International Paediatric Stroke Study
Bibliographic record
Abstract
Introduction: Stroke is reported among the top 10 causes of death in children in the US. In the Kids’ Inpatient Database, older age and Hispanic ethnicity were risk factors for mortality after pediatric ICH. Limited data is available regarding risk factors for death after pediatric arterial ischemic stroke (AIS). Objective: To identify predictors of in-hospital mortality in pediatric patients hospitalized with AIS. Methods: Neonates (0-28 days) and children (29 days- <19 years) with AIS were enrolled from 1/2003 to 7/2014 in the IPSS multinational stroke registry. Death prior to hospital discharge and cause of death was 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 74/2,285 children (3.2%) died during hospitalization. Of 54 cases with reported causes of death, 32 (59%) were related to AIS (herniation 2, brain death 10, ICH/hemorrhagic transformation 5, care withdrawal due to stroke severity 15), with the remaining deaths attributed to underlying medical disease. Of 356 children with Pediatric NIH Stroke Scale (PedNIHSS) scores, median PedNIHSS was 19 (IQR 14-27) among the 13 children who died and 7 (IQR 3-12) among the 343 children who did not die. In multivariable analysis, congenital heart disease (OR 4.1, 95%CI 1.3-13, p=0.018) and posterior plus anterior circulation stroke (OR 4.3, 95%CI 1.3-14, p=0.017) were associated with in-hospital mortality for neonates, while higher PedNIHSS [OR 1.11 (per 1 point PedNIHSS increase), 95%CI 1.03-1.19, p=0.004], Hispanic ethnicity (OR 7.6, 95%CI 1.8-32.3, p=0.006), and cardiac disease (OR 7.5, 95%CI 1.5-38.6, p=0.015) were associated with in-hospital mortality for children. Conclusions: In-hospital mortality occurred in about 2% of pediatric AIS cases with nearly 60% attributable to stroke. Risk factors for in-hospital mortality included cardiac disease and stroke severity, factors also associated with mortality in adults. Hispanic ethnicity, a factor associated with mortality in childhood ICH, was also associated with mortality after childhood AIS; the underlying reasons are unclear. Additional information is needed on stroke-related deaths after hospitalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".