A pediatric institutional acute stroke protocol improves timely access to stroke treatment
Bibliographic record
Abstract
AIM: We aimed to evaluate whether an institutional acute stroke protocol (ASP) could accelerate the diagnosis and secondary treatment of pediatric stroke. METHOD: We initiated an ASP in 2005. We compared 209 children (125 males, 84 females; median age 4.8y, interquartile range [IQR] 1.2-9.3y, range 0.09-17.7y) diagnosed with arterial ischemic stroke 'pre-protocol' (1992-2004) to 112 children (60 males, 52 females; median age 5.8y, IQR 1.0-11.4y, range 0.08-17.7y) diagnosed 'post-protocol' (2005-2012) for time-to-diagnosis, mode of diagnostic imaging, and time-to-treatment with antithrombotic medication (aspirin or anticoagulants). RESULTS: Overall, the interval from symptom onset to diagnosis was similar post-protocol compared to pre-protocol (20.3 vs 22.7h; p=0.109), although mild strokes (Pediatric National Institute of Health Stroke Scale [PedNIHSS] 0-4), were diagnosed faster post-protocol (12.1 vs 36.3h; p=0.003). Magnetic resonance imaging (MRI) was the initial diagnostic modality more often post-protocol (25% vs 1.4%; p<0.001). Initial MRI was more accurate for diagnosing stroke than initial CT (100% vs 47%; p<0.001) with similar time-to-diagnosis. The proportion of children receiving antithrombotic medication within 24 hours doubled in the post-protocol period (83% vs 36%; p<0.001). INTERPRETATION: A pediatric ASP accelerated time-to-treatment, time-to-diagnosis in children with subtle strokes, and increased MRI as initial imaging, reducing the need for computed tomography. Implementing optimized ASPs can facilitate more timely access to diagnosis and management of children with acute stroke.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".