Unruptured intracranial aneurysm treatment effects on cognitive function: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The treatment of an unruptured intracranial aneurysm (UIA) is not free of morbidity and mortality, and the decision is made by weighing the risks of treatment complications against the risk of aneurysm rupture. This meta-analysis quantitatively analyzed the literature on the effects of UIA treatment on cognition. METHODS: MEDLINE, Embase, and PsycInfo were systematically searched for studies that reported on the cognitive status of UIA patients before and after aneurysm treatment. The search was restricted to prospective cohort and case-control studies published between January 1, 1998, and January 1, 2013. The analyses focused on the effect of treatment on general cognitive functioning, with an emphasis on 4 specific cognitive domains: executive functions, verbal and visual memory, and visuospatial functions. RESULTS: Eight studies, with a total of 281 patients, were included in the meta-analysis. Treatment did not affect general cognitive functioning (effect size [ES] -0.22 [95% CI -0.78 to 0.34]). Executive functions and verbal memory domains trended toward posttreatment impairment (ES -0.46 [95% CI -0.93 to 0.01] and ES -0.31 [95% CI -1.24 to 0.61]), and performance of visual memory tasks trended toward posttreatment improvement (ES 1.48 [95% CI -0.36 to 3.31]). Lastly, treatment did not significantly affect visuospatial functions (ES -0.08 [95% CI -0.30 to 0.45]). CONCLUSIONS: The treatment of an UIA does not seem to affect long-term cognitive function. However, definitive conclusions were not possible due to the paucity of studies addressing this issue.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".