Clinical and angiographic outcomes of stent-assisted coiling of intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Despite the increasing use of stent-assisted coiling (SAC), data on its long-term clinical and angiographic results are limited. OBJECTIVE: The objective of this article is to assess the long-term clinical and angiographic outcomes in SAC in our single-center practice. METHODS: We conducted a retrospective analysis of intracranial aneurysms treated with detachable coils during the period 2003-2012. Patients were divided into SAC and non-SAC groups and were analyzed for aneurysm occlusion, major recurrence and clinical outcome. Logistic regression analyses identified factors associated with clinical/angiographic outcomes (p value <0.05 was statistically significant). RESULTS: A total of 516 procedures met inclusion criteria: Sixty-three (12.2%) patients underwent SAC, of whom 56 (89%) had an elective procedure whereas 286 (63.1%) aneurysms from the non-SAC group were ruptured. In the unruptured subcohort, baseline class I was achieved in 24 (38%, p = 0.91), and predischarge modified Rankin scale score (mRS) 0-2 was obtained in 96.4% of cases in the SAC group versus 90.4% in the non-stent group. The major recurrence was 9.5% versus 11.3% in the SAC and non-SAC group, respectively (p = 0.003). At last clinical assessment, 98.2% of the patients from the unruptured SAC group had mRS 0-2 (mean follow-up, 58 months) versus 93.6% (mean follow-up, 56 months) in the unruptured non-SAC group (p = 0.64). Periprocedural vasospasm was associated with long-term poor outcome in the unruptured SAC subcohort (p = 0.0008). CONCLUSIONS: SAC and non-SCA techniques show comparable safety and clinical outcome. The SAC technique significantly decreases retreatment rates. Periprocedural vasospasm resulting from vessel manipulation is associated with poor outcome in SAC of unruptured aneurysms.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| 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".