Anti-seizure medication exposure and the risk of dementia: A meta-analysis of observational studies
Bibliographic record
Abstract
Objective There is growing evidence of a relationship between anti-seizure medication (ASM) use and the risk of dementia. This study examined this association using a meta-analysis approach. Methods PubMed, EMBASE, and Cochrane Library were systematically searched for peer-reviewed observational studies published up to February 2023. Study quality was evaluated using the Newcastle-Ottawa Scale, and an overall odds ratio (OR) was pooled using fixed or random-effects models. Results The analysis included 9 publications with 10 studies. The results showed that overall ASM exposure was associated with an increased risk of dementia [OR: 1.09, 95% confidence interval (CI): 1.03–1.15; P = 0.003] in general population. However, this association disappeared (OR: 1.02, 95% CI: 0.97–1.07; P = 0.361) when the study data adjusted for drug indications were pooled. Subgroup analysis based on individual drugs found only a positive association among those exposed to valproate, carbamazepine, and clonazepam. Furthermore, an increased risk was found in patients with bipolar disorder exposed to ASMs (OR: 1.43, 95% CI: 1.07–1.92; P = 0.015). Conclusions The statistically significant association between ASM and dementia in general population may be driven by unmeasured confounding or several individual first-generation ASMs. However, a higher risk of dementia was observed among bipolar disorder patients treated with ASMs. Given the few included studies and evidence of high heterogeneity, further larger, prospective studies that control for important confounders are needed to verify our findings.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.057 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".