CE-MRA for Follow-up of Aneurysms Post Stent-Assisted Coiling
Bibliographic record
Abstract
This study compared the accuracy of contrast-enhanced MR angiography (CE-MRA) to intra-arterial cerebral angiography (IA-DSA) for assessment of intracranial aneurysms after stent-assisted coiling and to check if the presence of a stent in the parent artery diminishes the accuracy of CE-MRA. Consecutive patients with cerebral aneurysms treated by stent-assisted coiling were evaluated retrospectively. Matching follow-up CE-MRA and IA-DSA were evaluated separately. Evaluation included the presence of aneurysmal remnant, patency and stenosis of parent artery. Twenty-seven patients with 28 aneurysms and 33 matched CE-MRA and IA-DSA studies were evaluated. Nineteen aneurysmal remnants were seen on CE-MRA and 16 on IA-DSA. CE-MRA diagnosed three aneurysmal remnants not appreciated on IA-DSA. Five other remnants were larger on CE-MRA than IA-DSA. None of the remnants were missed on CE-MRA. Parent arteries were patent on both modalities. CE-MRA showed false stenosis of the stented artery in six cases and exaggerated stenosis in two. In 18 cases, CE-MRA showed a short focal "pseudo-stenosis" where the stent's marker bands were located. This was noted whenever the stent's marker bands were located in an artery with luminal diameter ≤2 mm and was called "marker band effect". CE-MRA is an accurate technique for follow-up of aneurysms post stent-assisted coiling with excellent depiction of remnants in spite of the presence of a stent. Apparent stenosis of the stented parent artery on CE-MRA is often false or exaggerated. "Marker band effect" should be recognized as an artifact that appears when stent's marker bands are in a small artery.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".