Unruptured intracranial aneurysm follow-up and treatment after morphological change is safe: observational study and systematic review
Bibliographic record
Abstract
BACKGROUND: The management of small unruptured incidentally discovered intracranial aneurysms (SUIAs) is still controversial. The aim of this study is to assess the safety of a management protocol of SUIAs, where selected cases with SUIAs are observed and secured only if signs of instability (growth) are documented. METHODS: A prospective consecutive cohort of 292 patients (2006-2014) and 368 SUIAs (anterior circulation aneurysms (ACs) smaller than 7 mm and posterior circulation aneurysms smaller than 4 mm without previous subarachnoid haemorrhage) was observed (mean follow-up time of 3.2 years and 1177.6 aneurysm years). Factors associated with aneurysm growth were systematically reviewed from the literature. RESULTS: The aneurysm growth probability was 2.6±0.1% per year. The rate of unexpected aneurysm rupture before treatment was 0.24% per year (95% CI 0.17% to 2.40%). The calculated rate of aneurysm rupture after growth was 6.3% per aneurysm-year (95% CI 1% to 22%). Aneurysms located in the posterior circulation and aneurysms with lobulation were more likely to grow. Females or patients suffering hypertension were more likely to have an aneurysm growing. The probability of aneurysms growth increased with the size of the dome and was proportional to the number of aneurysms diagnosed in a patient. CONCLUSIONS: It is safe to observe patients diagnosed with SUIAs using periodic imaging. Intervention to secure the aneurysm should be performed after growth is observed.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".