Multipsychiatric Comorbidity in People With Epilepsy Compared With People Without Epilepsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Persons with epilepsy (PwE) have a higher risk of developing psychiatric comorbidities compared with the general population. There is limited knowledge about the prevalence of multiple psychiatric conditions in PwE. We summarize the current evidence on the prevalence of multipsychiatric comorbidities in PwE compared with persons without epilepsy. METHODS: A systematic review of multipsychiatric comorbidities in PwE compared with persons without epilepsy was performed, and the results were reported using the Preferred Reporting Items of Systematic Reviews and Meta-analyses reporting standards. The search was conducted from January 1945 to June 2023 in Ovid MEDLINE. Embase, and PsycINFO, using the search terms related to "epilepsy," "psychiatric comorbidity," and "multimorbidity," combined with psychiatric disorders. Abstracts were reviewed in duplicate, and data were independently extracted using standard proforma. Data describing multipsychiatric comorbidities in PwE compared with persons without epilepsy were recorded. Descriptive statistics and, when feasible, meta-analyses are presented. The risk of bias of the studies was assessed using the Newcastle-Ottawa Scale and the International League Against Epilepsy tool. RESULTS: -value for heterogeneity = 0.79). DISCUSSION: PwE experience elevated levels of multipsychiatric comorbidity compared with those without epilepsy. However, very few studies have empirically evaluated the extent of multipsychiatric comorbidity in PwE compared with persons without epilepsy nor their associations and consequences to prognosis in PwE.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".