Repeat Flow Diversion for Cerebral Aneurysms Failing Prior Flow Diversion: Safety and Feasibility From Multicenter Experience
Bibliographic record
Abstract
BACKGROUND: Aneurysmal persistence after flow diversion (FD) occurs in 5% to 25% of aneurysms, which may necessitate retreatment. There are limited data on safety/efficacy of repeat FD-a frequently utilized strategy in such cases. METHODS: A series of consecutive patients undergoing FD retreatment from 15 centers were reviewed (2011-2019), with inclusion criteria of repeat FD for the same aneurysm at least 6 months after initial treatment, with minimum of 6 months post-retreatment imaging. The primary outcome was aneurysmal occlusion, and secondary outcome was safety. A multivariable logistic regression model was constructed to identify predictors of incomplete occlusion (90%-99% and <90% occlusion) versus complete occlusion (100%) after retreatment. RESULTS: >0.99). On multivariable regression, fusiform morphology had higher nonocclusion odds after retreatment (odds ratio [OR], 7.2 [95% CI, 1.97-20.8]). Family history of aneurysms was associated with lower odds of nonocclusion (OR, 0.18 [95% CI, 0.04-0.78]). Likewise, positive smoking history was associated with lower odds of nonocclusion (OR, 0.29 [95% CI, 0.1-0.86]). History of hypertension trended toward incomplete occlusion (OR, 3.10 [95% CI, 0.98-6.3]), similar to incorporated branch into aneurysms (OR, 2.78 [95% CI, 0.98-6.8]). CONCLUSIONS: Repeat FD for persistent aneurysms carries a reasonable success/safety profile. Satisfactory occlusion (100% and 90%-99% occlusion) was encountered in two-thirds of patients, with similar complications between the initial and subsequent retreatments. Fusiform morphology was the strongest predictor of retreatment failure.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".