Is It Possible to Recognize Cervical Artery Dissection on Stroke Brain MR Imaging? A Matched Case-Control Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Extracranial CAD accounts for nearly 20% of cases of stroke in young adults. The mural hematoma frequently extends cranially to the petrous carotid segment in cCAD or is distally located in vCAD. We hypothesized that standard brain MR imaging could allow the early detection of CAD of the upper portion of carotid and vertebral arteries. MATERIALS AND METHODS: Our prospectively maintained stroke data base was retrospectively queried to identify all patients with the final diagnosis of CAD. In the 103 consecutive patients studied, analysis of cervical fat-suppressed T1-weighted sequences demonstrated that the mural hematoma was located in the FOV of brain MR imaging in 77 patients. Subsequent to enrollment of a patient, a control patient was extracted from the same data base, within a similar categories for sex, age, NIHSS score, and stroke on DWI. Two blinded observers independently reviewed the 5 brain MR sequences of each examination and determined whether a CAD was present. RESULTS: Fifty-nine of the 77 patients with CAD (76.6%) and 73 of the 77 patients without CAD (94.8%) were correctly classified. Brain MR imaging demonstrated cCAD more frequently than vCAD in 54/58 (93.1%) and 5/19 (26.3%) patients, respectively, (P < .0001). CONCLUSIONS: Initial brain MR imaging can correctly suggest CAD in more than two-thirds of patients. This may have practical implications in patients with stroke with delayed cervical MRA or in those who are not initially suspected of having CAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".