Antiepileptic drugs and risk of suicide attempts: a case–control study exploring the impact of underlying medical conditions
Bibliographic record
Abstract
PURPOSE: Randomized-controlled trials and claims databases suggest that antiepileptic drug (AED) use may increase the risk of suicide attempts (SA). The present case-control study explores the impact of underlying indications on this potential association. METHODS: Physicians collected the medical history; prior 12-month drug use was obtained from standardized telephone interviews with patients. The association between AED use and SA was explored using multivariate conditional logistic regression. The analyses were replicated after stratification on depression and neurological disorders (epilepsy, migraine, and chronic neuropathic pain). RESULTS: Between 2008 and 2012, 506 adults with an incident SA were recruited in suicide treatment centers from across France and socio-demographically matched to 2829 controls from primary care settings. The association between AED use and odds of SA was not significant overall (odds ratio [OR], 1.5; 95% confidence interval [CI], 0.9-2.4). No association was observed for patients with neurological disorders (OR, 1.1; 95%CI, 0.5-2.4) as opposed to patients with depression (OR, 1.6; 95%CI, 1.0-2.5), but unmeasured confounding was suspected. CONCLUSIONS: Our results suggest that the association observed between AED use and increased odds of non-fatal SA in patients with either a lifetime history of depression or no neurological disorder may be explained by the presence of an underlying psychiatric disorder. Accounting for underlying indications is crucial in drug safety studies, as these can cause a reported association (or lack thereof) to be misleading. This may require the prospective collection of medical data at a patient level. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".