The FDA alert on suicidality and antiepileptic drugs: Fire or false alarm?
Bibliographic record
Abstract
In January 2008, the U.S. Food and Drug Administration (FDA) issued an alert about an increased risk for suicidality in 199 clinical trials of 11 antiepileptic drugs (AEDs) for three different indications, including epilepsy. An advisory panel voted against a black-box warning on AED labels, and the FDA has accepted this recommendation. We discuss three potential problems with the alert. First, adverse event data were used rather than systematically collected data. Second, the 11 drugs grouped together as a single class of AEDs have different mechanisms of action and very different relative risks, many of which were not statistically significant and some of which were smaller than one. These facts suggest that they should not be grouped as a class. Third, the risk of adverse effects from uncontrolled seizures almost certainly outweighs the small risk of suicidality. We place our comments in the context of a review of the literature on suicidality and depression in epilepsy and the sparse literature on AEDs and suicidality. We recommend that all patients with epilepsy be routinely evaluated for depression, anxiety, and suicidality, and that future clinical trials include validated instruments to systematically assess these conditions to determine whether the possible signal observed by the FDA is real.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".