Alcohol consumption, unprovoked seizures, and epilepsy: A systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to analyze and quantify the association between alcohol consumption and epilepsy as an independent disease, in part operationalized by the occurrence of unprovoked seizures, as well as to examine causality. METHODS: Systematic review, meta-analysis. RESULTS: A strong and consistent association between alcohol consumption and epilepsy/unprovoked seizures was found with an overall relative risk (RR) of 2.19 [95% confidence interval (CI) 1.83-2.63]. There was a dose-response relationship between the amount of alcohol consumed daily and the probability of the onset of epilepsy. Individuals consuming an average of four, six, and eight drinks daily had RRs of 1.81 (95% CI 1.59-2.07), 2.44 (95% CI 2.00-2.97), and 3.27 (95% CI 2.52-4.26), respectively, compared to nondrinkers. Several pathogenic mechanisms for the development of epilepsy in alcohol users were identified. Most of the relevant studies found that a high percentage of alcohol users with epilepsy would qualify for the criteria of alcohol dependence. Data were inconclusive regarding a threshold for the effect of alcohol, but most studies suggest that the effect may only hold for heavy drinking (four and more drinks daily). DISCUSSION: The relationship between alcohol consumption and epilepsy and unprovoked seizures was quantified and several pathogenic mechanisms were suggested, although none of them has been proven to be the unique causative pathway for epilepsy. Certain limitations underlying this study require further research to clarify the outstanding statistical issues and pathogenesis of epilepsy in heavy drinkers.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| 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".