The prevalence of anxiety and associated factors in persons with epilepsy
Bibliographic record
Abstract
The objectives of this study were to estimate the prevalence of, and factors associated with, anxiety in epilepsy. We conducted a cross-sectional analysis using data from the Neurological Disease and Depression Study. The prevalence of anxiety and associated factors were assessed using descriptive statistics and logistic regression. Of the total sample (n = 250 patients), nearly 40.0% of participants had anxiety according to the Hospital Anxiety and Depression Scale. The most prevalent symptom of anxiety was "worrying thoughts" (35.6%). After adjustment for age and sex, depression (odds ratio [OR] = 8.97, 95% confidence interval [CI] = 4.38-18.40), medication side effects (OR = 1.79, 95% CI = 1.04-3.05), smoking (OR = 4.35, 95% CI = 2.27-8.31), and illicit substance use (OR = 2.42, 95% CI = 1.18-4.96) were significantly associated with higher odds of anxiety, whereas higher education (OR = 0.47, 95% CI = 0.28-0.80) was associated with lower odds of anxiety. Furthermore, participants with anxiety reported more severe epilepsy, debilitating seizures, and overall lower quality of life. Evidence from our study reveals a high prevalence of anxiety in persons with epilepsy and that anxiety is associated with a variety of negative outcomes. These findings further emphasize the need for more studies to understand the impact of anxiety and its relationship with various sociodemographic and clinical factors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".