Report of the WPA section of pharmacopsychiatry on the relationship of antiepileptic drugs with suicidality in epilepsy
Bibliographic record
Abstract
INTRODUCTION: This report from the World Psychiatric Association Section on Pharmacopsychiatry examines the possible relationship of antiepileptic drugs with suicide-related clinical features and behaviors in patients with epilepsy. MATERIALS AND METHODS: A systematic review of the MEDLINE search returned 1039 papers, of which only 8 were considered relevant. A critical analysis of the Food and Drug Administration (FDA) report on the increase risk for patients under antiepileptics to manifest suicidality is also included in this report. RESULTS: The analysis of these studies revealed that the data are not supportive of the presence of a "class effect" on suicide-related behavior; on the contrary, there are some data suggesting such an effect concerning treatment with topiramate, lamotrigine, and levetiracetam for which further research is needed. DISCUSSION: For the majority of people with epilepsy, anticonvulsant treatment is necessary and its failure for any reason is expected to have deleterious consequences. Therefore, clinicians should inform patients and their families of this increased risk of suicidal ideation and behavior, but should not overemphasize the issue. Specific subgroups of patients with epilepsy might be at a higher risk, and deserve closer monitoring and follow-up. Future research with antiepileptics should specifically focus on depression and suicidal thoughts.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.007 | 0.001 |
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".