Patient- and Aneurysm-Specific Risk Factors for Intracranial Aneurysm Growth : A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE—: Follow-up imaging is often performed in intracranial aneurysms that are not treated. We performed a systematic review and meta-analysis on patient- and aneurysm-specific risk factors for aneurysm growth. METHODS—: We searched EMBASE and MEDLINE for cohort studies describing risk factors for aneurysm growth. Two authors independently assessed study eligibility and rated quality with the Newcastle Ottawa Scale. With univariable Poisson regression analysis, we calculated risk ratios (RRs) with corresponding 95% confidence intervals (95% CI) of risk factors for aneurysm growth. Heterogeneity was assessed with I. RESULTS—: Eighteen studies on 15 patient-populations described 3990 patients with 4972 unruptured aneurysms. A total of 437 aneurysms (9%) enlarged during 13 987 aneurysm-years of follow-up. Compared with aneurysms ≤4 mm, RRs were 2.56 (95% CI, 1.93–3.39; I=98%) for ≥5 mm, 2.80 (95% CI, 2.01–3.90; I=96%) for ≥7 mm, and 5.38 (95% CI, 3.76–7.70; I=97%) for ≥10 mm. Compared with aneurysms on the middle cerebral artery, the RR for basilar artery was 1.94 (95% CI, 1.32–2.83; I=57%). RRs were 2.03 (95% CI, 1.52–2.71; I=59%) for smoking at baseline, 2.04 (95% CI, 1.56–2.66; I=90%) for multiple unruptured aneurysms, 1.26 (95% CI, 0.97–1.62; I=59%) for women, 1.24 (95% CI, 0.98–1.58; I=40%) for hypertension, and 2.32 (95% CI, 1.46–3.68; I=91%) for irregular aneurysm shape. Compared with other regions, RR was 0.75 (95% CI, 0.58–0.96) for Japan and 0.64 (95% CI, 0.45–0.90) for Finland. CONCLUSIONS—: Most risk factors for aneurysm growth are consistent with risk factors for rupture. In contrast with rupture, the risk of growth was smaller in Japanese and Finnish cohorts compared with other regions. Pooling of individual patient data from low- and high-risk geographical regions is needed to assess independent predictors of aneurysm growth.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".