SILK flow diverter for complex intracranial aneurysms: a Canadian registry
Bibliographic record
Abstract
INTRODUCTION: The SILK flow diverter (SFD) is used for the treatment of complex intracranial aneurysms. Small case series have been reported in the literature but few studies with a large number of patients have been published. We present our experience with the SFD for the treatment of intracranial aneurysms in Canada. METHODS: Centers across Canada using SFDs were contacted to fill out a case report form for patients treated with an SFD in their center. Individual centers were responsible for approval from their ethics committee. Image analysis was performed by individual operators. The case report forms were collected and the final analysis was performed. RESULTS: A total of 92 patients were treated with SFDs in eight centers in Canada between January 2009 and August 2013. The aneurysms were located in the posterior circulation in 16 patients and in the anterior circulation in 76 patients. Most aneurysms (75%) were saccular in shape; 22% were fusiform and 3% were blister aneurysms. The size of the aneurysms varied from 2 to 60 mm with the neck varying from 2 to 60 mm. Perioperative morbidity and mortality were 8.7% and 2.2%, respectively. At the last available follow-up, 83.1% of the aneurysms were either completely or near-completely occluded. The rate of complications was higher for fusiform aneurysms (p<0.001). CONCLUSIONS: The SFD appears to be an important tool for the treatment of complex intracranial aneurysms. Treatment outcomes and complication rates remain a problem, but should be considered in the context of available alternative interventions. Ongoing analysis of flow-diverting stents for radiographic and clinical performance is required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".