Stroke in children
Bibliographic record
Abstract
OBJECTIVE: To characterize the risk factors for stroke in children and their relationship to outcomes. METHODS: We reviewed charts of children with ischemic and hemorrhagic stroke seen at Hopital Sainte-Justine, Montreal between 1991 and 1997. RESULTS: We found 51 ischemic strokes: 46 arterial and 5 sinovenous thromboses. Risk factors were variable and multiple in 12 (24%) of the 51 ischemic strokes. Ischemic stroke recurred in 3 (8%) patients with a single or no identified risk factor and in 5 (42%) of 12 patients with multiple risk factors (p = 0.01). We also found 21 hemorrhagic strokes, 14 (67%) of which were caused by vascular abnormalities. No patient with hemorrhagic stroke had multiple risk factors. Hemorrhagic stroke recurred in two patients (10%). Outcome in all 72 stroke patients was as follows: asymptomatic, 36%; symptomatic epilepsy or persistent neurologic deficit, 45%; and death, 20%. Death occurred more frequently in patients with recurrent stroke (40%) than in those with nonrecurrent stroke (16%). CONCLUSIONS: Multiple risk factors are found in many ischemic strokes and may predict stroke recurrence. Recurrent stroke tends to increase rate of mortality. Because of the high prevalence and importance of multiple risk factors, a complete investigation, including hematologic and metabolic studies and angiography, should be considered in every child with ischemic stroke, even when a cause is known.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".