Morphological, Hemodynamic, and Clinical Independent Risk Factors for Anterior Communicating Artery Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The pathogenesis of cerebral aneurysms still raises some controversies. The aim of this study was to identify morphological, hemodynamic, and clinical independent risk factors for anterior communicating artery (ACoA) aneurysm development. METHODS: Computed tomography angiography and transcranial color-coded sonography were performed in 77 patients with a nonbleeding ACoA aneurysm and in 73 controls. Symmetry of A1 segments of the anterior cerebral arteries, angles between A1 and A2 segments, tortuosity, diameter, mean velocity (Vm), pulsatility index, and volume flow rate in both A1 segments were determined. Moreover, all study participants completed a survey on their medical history. Multivariate backward stepwise logistic regression analysis was performed to identify independent risk factors for ACoA aneurysm development. RESULTS: Smoking, hypertension, asymmetry of A1 segments, the angle between A1 and A2 segments, A1 segment diameter, Vm, pulsatility index, and volume flow rate turned out to be associated with the occurrence of ACoA aneurysms on univariate analysis. Multivariate analysis identified smoking (odds ratio, 2.036; 95% confidence interval, 1.277-3.245), asymmetry of A1 segments>40% (odds ratio, 2.524; 95% confidence interval, 1.275-4.996), pulsatility index (odds ratio, 0.004; 95% confidence interval, 0.000-0.124), and the angle between A1 and A2 segments≤100° (odds ratio, 4.665; 95% confidence interval, 2.247-9.687) as independent strong risk factors for ACoA aneurysm development. CONCLUSIONS: The risk of ACoA aneurysm formation is determined by several independent clinical, morphological, and hemodynamic factors. The strongest independent risk factors include smoking, asymmetry of A1 segments>40%, low blood flow pulsatility, and the angle between A1 and A2 segments≤100°.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".