Seizure Risk Associated With Antidepressant Treatment Among Patients With Depressive Disorders
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of seizure associated with antidepressant use among patients with depressive disorders. METHODS: Individuals visiting the emergency department or hospitalized because of new-onset seizure (ICD-9-CM diagnostic code 345 or 780.3; our primary study outcome) after receiving antidepressants for depressive disorders, were identified from a Taiwanese total population health insurance database. Using a case-crossover study design, relative risk of antidepressant-related seizure was estimated by comparing the rates of antidepressant exposure during the case periods vs control periods. The effects of class and dose of antidepressant on seizure risk were explored, using a conditional logistic regression model adjusting for concomitant medications. Several sensitivity analyses were conducted to attest the results of primary analyses. RESULTS: A total of 10,002 patients were included between 2002 and 2012. Overall, antidepressant exposure was positively associated with increased seizure risk (OR = 1.48, 95% CI, 1.33-1.64). Among the antidepressants, the increases in seizure risk of bupropion (OR = 2.23, 95% CI, 1.58-3.16), selective serotonin reuptake inhibitors (OR = 1.76, 95% CI, 1.55-2.00), serotonin and norepinephrine reuptake inhibitors (OR = 1.40, 95% CI, 1.10-1.78), and mirtazapine (OR = 1.38, 95% CI, 1.08-1.77) showed clear dose-response effects. Furthermore, the seizure risk was highest among patients aged between 10 and 24 years and patients with major depression. The results of sensitivity analyses largely confirmed those from the primary analyses. CONCLUSIONS: The seizure-inducing propensity and dose-response relationship pattern, as well as potential risk factors, associated with individual antidepressants should be taken into consideration when choosing antidepressants during clinical practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".