Effect of Carotid Siphon Anatomy on Aneurysm Occlusion After Flow Diversion for Treatment of Internal Carotid Artery Aneurysms
Bibliographic record
Abstract
BACKGROUND: Flow diversion (FD) is effective for treatment of intracranial internal carotid artery (ICA) aneurysms. OBJECTIVE: To determine whether carotid siphon (CS) geometry influences the efficacy of FD when employed for ICA aneurysms. METHODS: Outcomes of a consecutive series of patients with ICA aneurysms treated with FD were retrospectively reviewed. CS anatomy was quantified through measurement of the posterior, anterior, and anterosuperior bend angles in accordance with previously described methodology. The relationship of CS geometry to likelihood of incomplete aneurysm occlusion at 1 yr after treatment was assessed with multivariate logistic regression analysis. RESULTS: There were 167 ICA aneurysms in 164 patients treated with FD during the study period. The mean age of our cohort was 55.7 yr (standard deviation [SD]: 12.3) and a majority of patients were female (145/164, 86.8%). Anterior (47.4 degrees vs 8.5, P < .001) and anterosuperior bend angles (100.9 vs 76.5, P = .002) were significantly greater in aneurysms that required repeat FD after initial treatment. On multivariate logistic regression analysis, increasing patient age (unit odds ratio [OR]: 1.05, 95% confidence interval [CI] 1.01-1.08; P = .003) and anterior bend angle ≥-3.5 (OR: 2.47, 95% CI 1.04-5.86; P = .046) were associated with increased odds of incomplete aneurysm occlusion at 1 yr after treatment. CONCLUSION: These findings suggest that variations in CS anatomy may influence the efficacy of FD treatment and should be analyzed prior to offering FD for treatment of ICA aneurysms. Further investigation into the hemodynamic effects of CS geometry is warranted.
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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 | 0.000 | 0.000 |
| 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".