Influence of comorbid anxiety and depression disorder on cognition in older adults with epilepsy
Bibliographic record
Abstract
Objective This study aims to investigate the association of comorbid depression and anxiety with cognitive function in older adults with epilepsy. Methods A cross-sectional analysis was conducted on 406 older adults (≥65 years) diagnosed with epilepsy between January 2019 and December 2020. Depressive and anxious symptoms were measured using the Hospital Anxiety and Depression Scale (HADS), while cognitive impairment was assessed with the Montreal Cognitive Assessment Test (MoCA). Multivariate linear regression models were used to examine associations between cognitive impairment and anxiety/depression symptoms, adjusting for potential confounders. Results Of the 406 adults, 218 (53.7%) showed cognitive impairment. Adults with depression (70.2% vs. 51.0%, P<0.01) or anxiety (66.7% vs. 48.8%, P<0.01) had a significantly higher prevalence of cognitive impairment compared to those without these conditions. Multivariate linear regression analysis revealed significant associations between cognitive impairment and depression (β=-1.77, 95% CI: -2.67, -0.87; P<0.01) and anxiety (β=-2.18, 95% CI: -2.95, -1.42; P<0.01). Conclusion Anxiety and depression are significantly associated with cognitive impairment in older adults with epilepsy. Early screening and management of these psychiatric conditions are essential to reduce cognitive decline and enhance patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".