Braid stability after flow diverter treatment of intracranial aneurysms: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The aim of this study was to evaluate the overall rates of braid changes associated with flow diverter (FD) treatment for intracranial aneurysms (IAs). Additionally, we sought to provide an overview of the currently reported definitions related to these complications. Methods A systematic search was conducted from the inception of relevant literature up to April 2023, encompassing six databases. The included studies focused on patients with IAs treated with FDs. We considered four main outcome measures as FD braid changes: (1) fish-mouthing, (2) device braid narrowing, (3) device braid collapsing, and (4) device braid deformation. The data from these studies were pooled using a random-effects model. Results A total of 48 studies involving 3572 patients were included in the analysis. Among them, 14 studies (39%) provided definitions for fish-mouthing. However, none of the included studies offered specific definitions for device braid narrowing, collapsing, or deformation, despite reporting rates for these complications in six, five, and three studies, respectively. The pooled rates for braid changes were as follows: 3% (95% CI 2% to 4%, I2=27%) for fish-mouthing, 7% (95% CI 2% to 20%, I2=85%) for narrowing, 1% (95% CI 0% to 3%, I2=0%) for collapsing, and 1% (95% CI 1% to 4%, I2=0%) for deformation. Conclusion The findings of this study suggest that FD treatment for IAs generally exhibits low rates of fish-mouthing, device braid narrowing, collapsing, and deformation. However, the lack of standardized definitions hinders the ability to compare device outcomes objectively, emphasizing the need for uniform definitions for FD braid changes in future prospective studies on FD.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".