Arterial Ischemic Strokes in Infants and Children: An Overview of Current Approaches
Bibliographic record
Abstract
Stroke in children is increasingly recognized. The incidence exceeds 8 per 100,000 per year. Important differences in stroke in newborns and children compared with adults, as well as a paucity of clinical trials, create challenges in the diagnosis and management of pediatric arterial ischemic stroke (AIS). The neurological presentation of AIS can be subtle. Radiographic diagnosis of acute AIS is also challenging because CT scan may be normal early on. Risk factors include vascular, intravascular, and embolic disorders; frequently, there are multiple risk factors in a given child, necessitating thorough investigations. More than 50% have a vasculopathy including postvaricella angiopathy, dissection, moyamoya, or vasculitis. Intravascular mechanisms are frequently present, including dehydration. Hematological or prothrombotic conditions are also associated with AIS in children, and include sickle cell disease and prothrombotic disorders. The latter have been identified in from one third to one half of children with AIS, are usually acquired, and frequently act in concert with other risk factors for stroke. The most common embolic source is congenital heart disease, which is present in 25% of children with AIS. Outcomes include death in 6% and neurological deficits in two thirds of children. Given that no clinical trials have been completed in pediatric stroke to date, treatment is empiric. Initial neuroprotective strategies aim to reduce the size of the infarct. For older children antithrombotic agents (antiplatelet drugs and anticoagulants) are given to reduce the 20 to 30% risk of recurrence. There are coordinated research efforts currently being initiated, which over the next decade will result in clinical trials in this understudied condition.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".