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 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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".