Patient and aneurysm characteristics in familial intracranial aneurysms. A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patient and aneurysm characteristics have been reported to differ between patients with familial and non-familial intracranial aneurysms (IAs), although results are inconsistent. We systematically reviewed and meta-analyzed the literature to identify and quantify patient- and aneurysm characteristics associated with familial IAs. METHODS: We searched PubMed and EMBASE for case-control and cohort studies comparing patient- and aneurysm characteristics between familial and non-familial IAs. Two observers independently assessed study eligibility and appraised quality with the Newcastle Ottawa Scale. With univariable weighted linear regression analysis we calculated β-coefficients with corresponding 95% confidence intervals (CIs) for ruptured and unruptured IAs combined and for ruptured IAs only. Heterogeneity was assessed with Higgins I2. RESULTS: A total of 15 articles were included in the meta-analysis in which 16,346 patients were analyzed with a total of 14,225 IAs. For ruptured and unruptured IAs combined, multiple IAs were more prevalent in familial (28.5%) than in non-familial IAs (20.4%; β = 0.10, 95% CI, 0.04 to 0.16; I2 0%). For ruptured IAs only, in familial patients IAs were more prevalent on the middle cerebral artery (41.1% versus 29.5%; β = 0.12, 95% CI, 0.01 to 0.24; I2 12%) and ruptured at a younger age (46.5 years versus 50.8 years; β = -5.00, 95% CI, -9.31 to -0.69; I2 98%) than in non-familial patients. No significant differences were found for the proportion of women, size of the aneurysm at time of rupture, smoking or hypertension. CONCLUSION: These results suggest that characteristics of familial and non-familial IAs show considerable overlap, yet differ on specific aspects. However, results for age at rupture showed considerable heterogeneity. These findings should be taken into consideration for future etiological research into IAs.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".