Safety and occlusion rates of surgical treatment of unruptured intracranial aneurysms: a systematic review and meta-analysis of the literature from 1990 to 2011
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Surgical clipping of unruptured intracranial aneurysms (UIAs) has recently been challenged by the emergence of endovascular treatment. We performed an updated systematic review and meta-analysis on the surgical treatment of UIAs, in an attempt to determine the aneurysm occlusion rates and safety of surgery in the modern era. METHODS: A detailed protocol was developed prior to conducting the review according to the Cochrane Collaboration guidelines. Electronic databases spanning January 1990-April 2011 were searched, complemented by hand searching. Heterogeneity was assessed using I(2), and publication bias with funnel plots. Surgical mortality and morbidity were analysed with weighted random effect models. RESULTS: 60 studies with 9845 patients harbouring 10 845 aneurysms were included. Mortality occurred in 157 patients (1.7%; 99% CI 0.9% to 3.0%; I(2)=82%). Unfavourable outcomes, including death, occurred in 692 patients (6.7%; 99% CI 4.9% to 9.0%; I(2)=85%). Morbidity rates were significantly greater in higher quality studies, and with large or posterior circulation aneurysms. Reported morbidity rates decreased over time. Studies were generally of poor quality; funnel plots showed heterogeneous results and publication bias, and data on aneurysm occlusion rates were scant. CONCLUSIONS: In studies published between 1990 and 2011, clipping of UIAs was associated with 1.7% mortality and 6.7% overall morbidity. The reputed durability of clipping has not been rigorously documented. Due to the quality of the included studies, the available literature cannot properly guide clinical decisions.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".