Late-onset epilepsy predicts future stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
Late-onset epilepsy (LOE) is closely associated with cerebrovascular disease. LOE also appears to be a harbinger of dementia. We performed a systematic review of observational studies, clinical studies and radiological studies relating to LOE. We conducted a meta-analysis of 5 observational studies and have found that patients presenting with LOE experience an increased risk of subsequent stroke, weighted OR 3.88 (95% CI 2.76 - 5.46). Above the age of fifty, incidence of new onset epilepsy is approximately 400/100,000 person years. Of these, 9.8–11.9% will have a stroke in the decade following epilepsy onset, translating to an incidence of «pre-stroke epilepsy» of 25–55/100,000 person years. The additional studies demonstrated clinical and radiological evidence to support the premise that LOE is likely to reflect underly- ing cerebrovascular disease. Cerebrovascular risk factors convey increased risk of LOE, and LOE can act as a «harbinger» for stroke and dementia, with various proposed pathophysiological mechanisms. The onset of LOE may represent a potential point for intervention, with the aim of stroke prevention. Current data supports the need for prospective research in order to better understand the natural history of LOE, disease mechanisms and opportunities for intervention to reduce the apparent sequelae of stroke and dementia. jasminewall@googlemail.com
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".