Cognitive dysfunction and its associated factors in patients with epilepsy at referral hospitals in the Amhara region: an institutional-based cross-sectional study
Bibliographic record
Abstract
Background Epilepsy is a multifactorial disease characterized by spontaneous, recurrent seizures and a growing incidence of comorbid conditions such as anxiety, depression, cognitive dysfunction, and sudden unexpected death. Patients with epilepsy often experience cognitive impairment or dysfunction that can negatively affect their quality of life. There is limited research on cognitive dysfunction assessed through the Montreal Cognitive Assessment (MoCA) in the Amhara region, although the MoCA is considered superior to the Mini-Mental State Examination (MMSE). Therefore, this study aimed to assess cognitive dysfunction and identify factors associated with it in patients with epilepsy who were receiving follow-up care at referral hospitals in the Amhara region. Materials and methods A multicenter, institutional-based cross-sectional study was conducted among patients with epilepsy who were receiving follow-up care at randomly selected referral hospitals in the Amhara region from January 2024 to July 2024. A total of 355 participants were recruited for the study using a systematic random sampling technique, achieving a response rate of 98%. Cognitive dysfunction was measured using the MoCA. Data were entered with EpiData version 4.7 and then exported into SPSS version 26 for analysis. Multivariable logistic regression analysis was conducted, and a p-value of ≤0.05 was considered statistically significant. The results are presented in text and tables. Results The majority of the participants were women (52.1%). The mean age of the study participants was 31 (± 5.4) years. The prevalence of cognitive dysfunction was 29% (95% CI: 25.8, 34.5). Multivariable logistic regression analysis revealed that several factors were statistically significantly associated with cognitive dysfunction. Factors associated with cognitive dysfunction included being a rural resident (adjusted odds ratios (AOR) = 1.21; 95% CI: 1.29, 1.43), having a medical illness (AOR = 2.5; 95% CI: 2.1, 9.1), experiencing generalized seizures (AOR = 1.3; 95% CI: 1.08, 3.1), having a seizure frequency of daily to every other day (AOR = 2; 95% CI: 1.5, 9.2), experiencing seizures for more than 30 years (AOR = 1.5; 95% CI: 1.7, 7.6), and using a combination of anti-seizure drugs (AOR = 2.5; 95% CI: 1.2, 6.2). Conclusions and recommendations In this study, a significant proportion of patients with epilepsy receiving follow-up care experienced cognitive dysfunction. Neuropsychological assessment should be emphasized in patients with epilepsy at diagnosis and early follow-up phases of the condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".