Added Value of High-Resolution MR Imaging in the Diagnosis of Vertebral Artery Dissection
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The optimal imaging method for the diagnosis of VAD remains undefined. Our aim was to evaluate the added value of HR-MR imaging for the diagnosis of VAD. MATERIALS AND METHODS: We retrospectively extracted 35 consecutive patients suspected of having acute VAD who had the following: 1) a focal lumen abnormality of the VA on CE-MRA, 2) HR-MR imaging during the initial hospital stay, and 3) clinical and imaging follow-up within 6 months. Two neurologists classified patients as either VAD (group A) or non-VAD (group B) by reviewing all the available data at hospital discharge, except HR-MR imaging data. On HR-MR imaging, 2 radiologists searched for signs of acute VAD. The 2 classifications were compared. In case of discordance, CE-MRA follow-up and axial fat-suppressed T1WI, used to obtain supportive evidence for or against VAD, were considered as the standard of reference. RESULTS: In 4/18 patients in group A, HR-MR imaging did not demonstrate any signs of acute VAD and perivertebral signal-intensity changes were attributed to venous plexus, with an unchanged lumen on follow-up. In 4/17 patients in group B, HR-MRI demonstrated a mural hematoma, with lumen normalization on follow-up CE-MRA. CONCLUSIONS: Our results encourage the use of HR-MR imaging as a second-line diagnostic tool in the event of suspicion of acute VAD and doubtful findings on standard imaging.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".