Endovascular treatment of cavernous carotid artery aneurysms: A 10-year, single-center experience
Bibliographic record
Abstract
Background Cavernous carotid artery aneurysms can be treated by several endovascular techniques including flow diversion (FD) and parent vessel occlusion (PVO). We reviewed our institution’s consecutive series of endovascularly treated cavernous carotid artery aneurysms to compare these two modalities and their associated clinical and radiographic outcomes. Methods All patients harboring a cavernous carotid artery aneurysm treated by FD or PVO from January 2008 to December 2018 were enrolled. Data were collected retrospectively and analyzed on patient presentation, aneurysm dimensions, treatments and related complications, rate of aneurysm occlusion, sac regression, and outcomes. Results Fourteen patients were treated with FD and 12 underwent PVO subsequent to passing a balloon test occlusion. There was no significant difference between treatment modalities in aneurysmal occlusion (97.0 ± 8.4% (FD) vs. 100% (PVO), p = 0.23), degree of sac regression (62.5 ± 16.7% (FD) vs. 56.8 ± 24.3% (PVO), p = 0.49), or near-complete to complete symptom improvement (66.7% (FD) vs. 81.8% (PVO), p = 0.62). Major complications included subarachnoid hemorrhage from aneurysmal rupture in 1 (7.1%) patient post-FD and 2 (16.7%) ischemic strokes following PVO. Conclusions Endovascular treatment of cavernous carotid artery aneurysms by FD or PVO are both effective and safe. There is insufficient evidence to recommend one technique over the other and decision making should be individualized to the patient, their aneurysm morphology, and operator experience.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".