1331 – Are Depression And Anxiety Related To Insomnia In Epilepsy Patients?
Bibliographic record
Abstract
Introduction Insomnia is a frequent symptom of depression and vice versa. Both are prevalent in epilepsy and can worsen the course of disease. Objectives To assess relationship of insomnia with depression and anxiety in epilepsy. Methods Adult patients with all-cause epilepsy diagnoses attending a tertiary epilepsy and sleep centers were interviewed regarding insomnia symptoms. Patients were divided into two groups: with (IG) and without (WIG) insomnia. Depression and anxiety were assessed by Hamilton’s depression (HAMD) and anxiety (HAMA) scales. T-test was used for statistics. Results 58 patients with epilepsy aged 18-64 (mean age - 33.8, 21 females - 36.2%) were enrolled. Among these patients 31 had insomnia complaints (53.4%). The groups did not differ in terms of mean age: 33.1 for IG and 34.3 for WIG (p>0.05). Mean values for HAMD and HAMA in the groups were as follows: HAMD - 16.3 for IG, 11.04 for WIG; HAMA - 18.3 for IG, 13.4 for WIG. For both scales there was an increase in levels of depression and anxiety for epilepsy patients with insomnia. The difference reached statistical significance for depression (p = 0.004). Anxiety was also numerically more prevalent in IG but this difference was not significant (p = 0.055). Conclusion Results of our study show that insomnia is a frequent co-morbidity in epilepsy. We found that depression is more prevalent in patients with concomitant epilepsy and insomnia than in patients without difficulties initiating or maintaining sleep. Anxiety also seems to be more marked in insomniac epilepsy patients but to a lesser degree.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 0.001 |
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".