Delay to Diagnosis in Acute Pediatric Arterial Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: For the clinician, the diagnosis of arterial ischemic stroke (AIS) in children is a challenge. Prompt diagnosis of pediatric AIS within 6 hours enables stroke-specific thrombolytic and neuroprotective strategies. METHODS: We conducted a retrospective study of prospectively enrolled consecutive cohort of children with AIS, admitted to The Hospital for Sick Children, Toronto, from January 1992 to December 2004. The data on clinical presentation, symptom onset, emergency department arrival, neuroimaging and stroke diagnosis were recorded. The putative predictors of delayed diagnosis were selected a priori for analysis. RESULTS: A total of 209 children with AIS were studied. The median interval from symptom onset to AIS diagnosis was 22.7 hours (interquartile range: 7.1 to 57.7 hours), prehospital delay (symptom onset to hospital arrival) was 1.7 hours (interquartile range: 49 minutes to 8.1 hours), and the in-hospital delay (presentation to diagnosis) was 12.7 hours (interquartile range: 4.5 to 33.5 hours). The initial assessment was completed in 16 minutes and initial neuroimaging in 8.8 hours. The diagnosis of AIS was suspected on initial assessment in 79 (38%) children and the initial neuroimaging diagnosed AIS in 47%. The parent's help seeking action, nonabrupt onset of symptoms, altered consciousness, milder stroke severity, posterior circulation infarction and lack of initial neuroimaging at a tertiary hospital were predictive delayed AIS diagnosis. CONCLUSIONS: In the diagnosis of AIS, significant prehospital and in-hospital delays exist in children. Several predictors of the delayed AIS diagnosis were identified in the present study. Efforts to target these predictors can reduce diagnostic delays and optimize the management of AIS in children.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".