Rupture of Very Small Intracranial Aneurysms: Incidence and Clinical Characteristics
Bibliographic record
Abstract
OBJECTIVE: Unruptured intracranial aneurysms are now being detected with increasing frequency in clinical practice. Results of the largest studies, including those of the International Study of Unruptured Intracranial Aneurysms, indicate that surgical and endovascular treatments are rarely justified in small aneurysms. However, we have encountered several cases of rupture of small and very small aneurysms in our clinical practice. This retrospective study analyzed the incidence and clinical characteristics of very small ruptured aneurysms. MATERIALS AND METHODS: A total of 200 patients with aneurysmal subarachnoid hemorrhage between January 2012 and December 2014 were reviewed. Various factors were analyzed, including the aneurysm location and size as well as the associated risk factors. RESULTS: The mean age of patients was 56.31 ± 13.78 (range, 25-89) years, and the male to female ratio was 1:2.1. There were 94 (47%) small-sized (< 5 mm), 91 (45.5%) medium-sized (5-9.9 mm), and 15 large-sized (> 10 mm) aneurysms. Of these, 30 (15%) aneurysms were very small-sized (< 3 mm). The most frequent site of aneurysms was the anterior communicating artery (ACoA). However, the proportion of aneurysms at the ACoA was significantly high in very small aneurysms (53.3%, p = 0.013). Hypertension was a significant risk factor for rupture of very small aneurysms (p < 0.001). CONCLUSION: About half of our cases of ruptured aneurysms involved the rupture of small and very small aneurysms. The most common site of rupture of very small aneurysm was the ACoA. Rupture of small and very small aneurysms is unpredictable, and treatment may be considered in selected high-risk patients according to factors such as young age, ACoA location, and hypertension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".