Comparison of Multidetector CT Angiography and MR Imaging of Cervical Artery Dissection
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Conventional angiography has been historically considered the gold standard for the diagnosis of cervical artery dissection, but MR imaging/MR angiography (MRA) and CT/CT angiography (CTA) are commonly used noninvasive alternatives. The goal of this study was to compare the ability of multidetector CT/CTA and MR imaging/MRA to detect common imaging findings of dissection. MATERIALS AND METHODS: Patients in the data base of our Stroke Center between 2003 and 2007 with dissections who had CT/CTA and MR imaging/MRA on initial work-up were reviewed retrospectively. Two neuroradiologists evaluated the images for associated findings of dissection, including acute ischemic stroke, luminal narrowing, vessel irregularity, wall thickening/hematoma, pseudoaneurysm, and intimal flap. The readers also subjectively rated each vessel on the basis of whether the imaging findings were more clearly displayed with CT/CTA or MR imaging/MRA or were equally apparent. RESULTS: Eighteen patients with 25 dissected vessels (15 internal carotid arteries [ICA] and 10 vertebral arteries [VA]) met the inclusion criteria. CT/CTA identified more intimal flaps, pseudoaneurysms, and high-grade stenoses than MR imaging/MRA. CT/CTA was preferred for diagnosis in 13 vessels (5 ICA, 8 VA), whereas MR imaging/MRA was preferred in 1 vessel (ICA). The 2 techniques were deemed equal in the remaining 11 vessels (9 ICA, 2 VA). A significant preference for CT/CTA was noted for VA dissections (P < .05), but not for ICA dissections. CONCLUSION: Multidetector CT/CTA visualized more features of cervical artery dissection than MR imaging/MRA. CT/CTA was subjectively favored for vertebral dissection, whereas there was no technique preference for ICA dissection. In many cases, MR imaging/MRA provided complementary or confirmatory information, particularly given its better depiction of ischemic complications.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".