Clinical, Radiological, and Flow-Related Risk Factors for Growth of Untreated, Unruptured Intracranial Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Unruptured intracranial aneurysms are frequently followed to monitor aneurysm growth. We studied the yield of follow-up imaging and analyzed risk factors for aneurysm growth. METHODS: We included patients with untreated, unruptured intracranial aneurysms and ≥6 months of follow-up imaging from 2 large prospectively collected databases. We assessed the proportion of patients with aneurysm growth and performed univariable and multivariable Cox regression analyses to calculate hazard ratios with corresponding 95% confidence intervals (CI) for clinical and radiological risk factors for aneurysm growth. We repeated these analyses for the subset of small (<7 mm) aneurysms. RESULTS: Fifty-seven (12%) of 468 aneurysms in 363 patients grew during a median follow-up of 2.1 years (total follow-up, 1372 patient-years). In multivariable analysis, hazard ratios for aneurysm growth were as follows: 1.1 (95% CI, 1.0-1.2) per each additional mm of initial aneurysm size; 2.7 (95% CI, 1.2-6.4) for dome > neck ratio; 2.1 (95% CI, 0.9-4.9) for location in the posterior circulation; and 2.0 (95% CI, 0.8-4.8) for multilobarity. In the subset of aneurysms <7 mm, 37 of 403 (9%) enlarged. In multivariable analysis, hazard ratios for aneurysm growth were 1.1 (95% CI, 0.8-1.5) per each additional mm of initial aneurysm size, 2.2 (95% CI, 1.0-4.8) for smoking, 2.9 (95% CI, 1.0-8.5) for multilobarity, 2.4 (95% CI, 1.0-5.8) for dome/neck ratio, and 2.0 (95% CI, 0.6-7.0) for location in the posterior circulation. CONCLUSIONS: Initial aneurysm size, dome/neck ratio, and multilobarity are risk factors for aneurysm growth. Cessation of smoking is pivotal because smoking is a modifiable risk factor for growth of small aneurysms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".