Flow Diversion for Middle Cerebral Artery Aneurysms: An International Cohort Study
Bibliographic record
Abstract
BACKGROUND: Open surgery has traditionally been preferred for the management of bifurcation middle cerebral artery (MCA) aneurysms. Flow diverting stents present a novel endovascular strategy for aneurysm treatment. OBJECTIVE: To add to the limited literature describing the outcomes and complications in the use of flow diverters for the treatment of these complex aneurysms. METHODS: This is a multicenter retrospective review of MCA bifurcation aneurysms undergoing flow diversion. We assessed post-treatment radiological outcomes and both thromboembolic and hemorrhagic complications. RESULTS: We reviewed the outcomes of 54 aneurysms treated with flow diversion. Four (7.4%) of the aneurysms had a history of rupture (3 remote and 1 acute). Fourteen (25.9%) of the aneurysms already underwent either open surgery or coiling prior to flow diversion. A total of 36 out of the 45 aneurysms (80%) with available follow-up data had adequate aneurysm occlusion with a median follow-up time of 12 mo. There were no hemorrhagic complications but 16.7% (9/54) had thromboembolic complications. CONCLUSION: Flow diverting stents may be a viable option for the endovascular treatment of complex bifurcation MCA aneurysms. However, compared to published series on the open surgical treatment of this subset of aneurysms, flow diversion has inferior outcomes and are associated with a higher rate of complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".