Psychiatric comorbidity in people with epilepsy in Ethiopia: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Psychiatric comorbidity is a double burden among people with epilepsy. Since people with epilepsy are more vulnerable to psychiatric illnesses. So, the implementation of an appropriate intervention to minimize the double burden of comorbidity is very important. Therefore, this systematic review and meta-analysis aimed to assess the prevalence and associated factors of psychiatric comorbidity among people with epilepsy in Ethiopia. METHODS: This systematic review and meta-analysis followed the Preferred Reporting Item Review and Meta-analysis (PRISMA) guideline. Searching databases were PubMed, PsycINFO, Web of Science, Cochrane Library, Google Scholar, and HINARI.The quality of the included articles was assessed using the Newcastle-Ottawa Scale (NOS). The pooled meta-logistic regression was computed to estimate the pooled prevalence and the risk factors with a 95% CI. RESULTS: The pooled prevalence of psychiatric comorbidity in people with epilepsy was 34.69 % (95 % CI: 29.27, 40.10). Frequent seizures (POR = 2.94: 95 % CI: 1.08, 8.00) and a history of divorce (POR = 2.00: 95 % CI: 1.09, 3.81) were associated factors of psychiatric comorbidity in people with epilepsy. CONCLUSIONS: This systematic review and meta-analysis revealed that the pooled prevalence of psychiatric comorbidity among people with epilepsy was found to be higher compared with the general population. Therefore, among people with epilepsy, parallel psychiatric evaluation is very important along with neurological evaluation.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".