Mortality in patients with epilepsy: a systematic review
Bibliographic record
Abstract
BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies. METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations. RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death. CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".