Psychiatric Comorbidity in Epilepsy: A Population‐Based Analysis
Bibliographic record
Abstract
PURPOSE: The estimated prevalence of mental health disorders in those with epilepsy in the general population varies owing to differences in study methods and heterogeneity of epilepsy syndromes. We assessed the population-based prevalence of various psychiatric conditions associated with epilepsy using a large Canadian national population health survey. METHODS: The Canadian Community Health Survey (CCHS 1.2) was used to explore numerous aspects of mental health in persons with epilepsy in the community compared with those without epilepsy. The CCHS includes administration of the World Mental Health Composite International Diagnostic Interview to a sample of 36,984 subjects. Age-specific prevalence of mental health conditions in epilepsy was assessed using logistic regression. RESULTS: The prevalence of epilepsy was 0.6%. Individuals with epilepsy were more likely than individuals without epilepsy to report lifetime anxiety disorders or suicidal thoughts with odds ratio of 2.4 (95% CI = 1.5-3.8) and 2.2 (1.4-3.3), respectively. In the crude analysis, the odds of lifetime major depression or panic disorder/agoraphobia were not greater in those with epilepsy than those without epilepsy, but the association with lifetime major depression became significant after adjustment for covariates. CONCLUSIONS: In the community, epilepsy is associated with an increased prevalence of mental health disorders compared with the general population. Epilepsy is also associated with a higher prevalence of suicidal ideation. Understanding the psychiatric correlates of epilepsy is important to adequately manage this patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".