Digital Subtraction Angiography‐Dynavision in Pretreatment Planning for Embolization of Dural Arterio‐Venous Fistulas
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We have found DSA-Dynavision with multiplanar reconstruction very helpful in understanding the complex anatomy and planning of treatment of carotico-cavernous fistulas. The purpose of our study was to examine whether using DSA-Dynavision in pretreatment planning results in better outcome after endovascular treatment of dural arterio-venous fistulas (dAVFs). METHODS: Patients with dAVF treated with endovascular embolization were retrospectively identified from our interventional neuroradiology database. Patients were assessed and divided into those with DSA-Dynavision and those without. They were compared for procedural time, angiographic evidence of cure, rates of resolution of cortical venous reflux (CVR), complications, and need for postembolization surgery. RESULTS: Eighty-six percent of 28 patients (mean age 57 years, range 1.67-84 years) had Borden type 3 DAVF; 7% had Borden type 2; and 7% had Borden type 1. DSA-Dynavision was used in 14 of 28 (50%) patients. Fewer patients with DSA-Dynavision required postendovascular embolization surgery (7% vs. 50%, P = .01) and fewer DSA-Dynavision patients had CVR postprocedure (29% vs. 71%, P = .023). Mean procedural time (207 vs. 249 minutes; P = .40); permanent neurological complication rates (7% vs. 7%, P = 1.0); rate of immediate angiographic occlusion (64% vs. 29%, P = .061), and reported resolution of symptoms (79% vs. 53%, P = .18) were not significantly different. There was no significant difference in follow-up (mean: 75 vs 120 weeks, P = .47). CONCLUSION: The use of DSA-Dynavision in planning of endovascular treatment of dAVF is associated with higher rates of elimination of CVR and less need for postembolization surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".