Arterial ischemic stroke risk factors: The international pediatric stroke study
Bibliographic record
Abstract
OBJECTIVE: To describe presumptive risk factors (RFs) for childhood arterial ischemic stroke (AIS) and explore their relationship with presentation, age, geography, and infarct characteristics. METHODS: Children (29 days-18 years) were prospectively enrolled in the International Pediatric Stroke Study. Risk factors, defined conditions thought to be associated with childhood AIS, were divided into 10 categories. Chi-square tests were used to compare RFs prevalence across regions and age; logistic regression was used to determine whether RFs were associated with particular features at presentation or infarct characteristics. RESULTS: A total of 676 children were included. No identifiable RFs was present in 54 (9%). RFs in others included arteriopathies (53%), cardiac disorders (CDs) (31%), infection (24%), acute head and neck disorders (AHNDs) (23%), acute systemic conditions (ASCs) (22%), chronic systemic conditions (CSCs) (19%), prothrombotic states (PTSs) (13%), chronic head and neck disorders (CHNDs) (10%), atherosclerosis-related RFs (2%), and other (22%). Fifty-two percent had multiple RFs. There was lower prevalence of arteriopathy in Asia, lower prevalence of CSCs in Europe and Australia, higher prevalence of PTSs in Europe, and higher prevalence of ASCs in Asia and South America. Prevalence of CDs and ASCs was highest in preschoolers, arteriopathies in children 5 to 9 years old, and CHNDs were highest in children aged 10 to 14 years. Arteriopathies were associated with focal signs and ASCs, CHNDs, and AHNDs with diffuse signs. Arteriopathies, CSCs, and ASCs were associated with multiple infarcts and CDs with hemorrhagic conversion. INTERPRETATION: RFs, especially arteriopathy, are common in childhood AIS. Variations in RFs by age or geography may inform prioritization of investigations and targeted preventative strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".