A protocol for a pooled analysis of cohort studies: The association between depression and anxiety in epileptic disorders
Bibliographic record
Abstract
BACKGROUND/INTRODUCTION: Depressive and anxiety disorders constitute major mental health challenges affecting adults of all ages globally. It has been reported that individuals with depressive or anxiety disorders face an elevated risk of developing neurological conditions, including seizures and epilepsy. Additionally, people with these disorders tend to exhibit distinct clinical outcomes compared to the general population. However, the associations between depressive or anxiety disorders and epilepsy remain contentious. Thus, this study aims to elucidate the associations between these neuropsychiatric disorders, including depressive and anxiety disorders, and epilepsy or seizures. METHODS: We will systematically search three electronic databases-PubMed, EMBASE, and the Cochrane Library-from inception through March 2023 to identify relevant cohort studies investigating the associations between depressive or anxiety disorders and epilepsy or seizures. Two independent reviewers will extract data from eligible studies using pre-designed standardized data extraction forms, and cross-check results. A third author will resolve any discrepancies. Quality assessment will be performed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Pooled risk estimates (Relative risks or hazard ratios with their 95% CI) will be calculated using the DerSimonian-Laird random-effects model. If between-study heterogeneity is identified, we will conduct subgroup analyses or meta-regressions to explore the possible sources of heterogeneity (participants, exposure, outcome, and study design) stratified by various study characteristics. Potential publication bias will be detected through the inspection of funnel plot asymmetry, complemented by the Egger linear regression approach (Egger's test) and the Begg rank correlation test (Begg's test). DISCUSSION: This pooled analysis will evaluate the association between depressive or anxiety disorders and epilepsy or seizures, providing high-level evidence to inform early identification and prevention strategies for epilepsy or seizures. ETHICS AND DISSEMINATION: Given that the data utilized for analysis in this pooled analysis does not involve human subjects or medical records, no ethical approval is required for this study. We intend to present the results of this study at national or international conferences or submit the findings to a peer-reviewed journal. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/WM2X8.
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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 | 0.169 | 0.261 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.087 | 0.016 |
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".