Low Detection Rate of Craniocervical Arterial Dissection in Children Using Time-of-Flight Magnetic Resonance Angiography: Causes and Strategies to Improve Diagnosis
Bibliographic record
Abstract
Craniocervical arterial dissection is a frequent cause of childhood arterial ischemic stroke requiring prompt diagnosis and treatment. However, there is no universal guideline for diagnostic cerebrovascular imaging in children. We assessed the role of time-of-flight magnetic resonance angiography in diagnosing craniocervical arterial dissection. We included children (1 month to 18 years) with craniocervical arterial dissection and ischemic stroke from January 1998 to June 2007. Institutional protocol required magnetic resonance imaging (MRI)/ magnetic resonance angiography for all ischemic stroke cases and conventional cerebral angiography if magnetic resonance angiography showed any arteriopathy. Among 233 ischemic stroke cases, craniocervical arterial dissection was diagnosed in 13 patients (5.6%; 10 males), with a mean age of 7.5 years. Initial time-of-flight magnetic resonance angiography missed dissection in 8 patients (61.5%) because the neck vessels were not scanned (n = 2), of suboptimal technique (n = 3), and of diagnostic error (n = 3). T1 fat-saturated MRI and contrast-enhanced magnetic resonance angiography were not performed in 12 patients. If suspicion for arterial dissection is high, T1 fat-saturated neck imaging and contrast-enhanced magnetic resonance angiography should be considered to maximize detection.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".