Second-generation anti-depressants and risk of new-onset seizures in the elderly
Bibliographic record
Abstract
BACKGROUND: Anti-depressants are among the most widely-prescribed medications. It is unknown whether the risk of seizure during therapeutic use differs by drug. We ranked the seizure risk of popular anti-depressants. METHODS: We conducted a population-based case-control study between April 2002 and March 2015 in Ontario, Canada. Cases were Ontario residents aged ≥65 years hospitalized for a first-ever seizure within 60 d of filling a prescription for one of nine second-generation anti-depressants, each dispensed more than 1 million times (range: 1,196,810 [fluvoxamine] to 19,849,930 [citalopram]) during the study period. For each case, we identified up to four seizure-free controls receiving a similar anti-depressant, and matched on age, sex, date and a pre-defined seizure-specific disease risk index. RESULTS: We identified 5701 patients hospitalized with a first-ever seizure and matched them with 21,872 controls. Relative to bupropion, the risk of new-onset seizure during therapeutic use was highest for escitalopram (adjusted odds ratio [OR] 1.79; 95% confidence interval [CI] 1.42-2.25) and citalopram (OR 1.67; 95% CI 1.35-2.07), while no incremental risk was found for fluoxetine (OR 1.02; 95%CI 0.78-1.33) and duloxetine (OR 0.94; 95%CI 0.75-1.22). Other anti-depressants were associated with modest increase in seizure risk. CONCLUSIONS: The risk of seizure during therapeutic use among elderly patients varies among second-generation anti-depressants. Escitalopram and citalopram are associated with the highest risk. Prescribers should consider the seizure risk of individual anti-depressants and use discretion when selecting an anti-depressant, especially for patients with other risk factors for seizure. Frontline clinicians should be cognizant of this differential risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".