Learning Curve for Flow Diversion of Posterior Circulation Aneurysms: A Long-Term International Multicenter Cohort Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Flow diversion has gradually become a standard treatment for intracranial aneurysms of the anterior circulation. Recently, the off-label use of the flow diverters to treat posterior circulation aneurysms has also increased despite initial concerns of rupture and the suboptimal results. This study aimed to explore the change in complication rates and treatment outcomes across time for posterior circulation aneurysms treated using flow diversion and to further evaluate the mechanisms and variables that could potentially explain the change and outcomes. MATERIALS AND METHODS: A retrospective review using a standardized data set at multiple international academic institutions was performed to identify patients with ruptured and unruptured posterior circulation aneurysms treated with flow diversion during a decade spanning January 2011 to January 2020. This period was then categorized into 4 intervals. RESULTS: A total of 378 procedures were performed during the study period. Across time, there was an increasing tendency to treat more vertebral artery and fewer large vertebrobasilar aneurysms (P = .05). Moreover, interventionalists have been increasingly using fewer overlapping flow diverters per aneurysm (P = .07). There was a trend toward a decrease in the rate of thromboembolic complications from 15.8% in 2011–13 to 8.9% in 2018–19 (P = .34). CONCLUSIONS: This multicenter experience revealed a trend toward treating fewer basilar aneurysms, smaller aneurysms, and increased usage of a single flow diverter, leading to a decrease in the rate of thromboembolic and hemorrhagic 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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".