On the Relevance of Seizure Activity to Suicide Risk
Bibliographic record
Abstract
It is evident to researchers that the detection of the increased risk of suicide, as a result of the use of certain psychotropics, is a difficult task due to the inherently high risk of suicide in patients prescribed these medications. Selective serotonin reuptake inhibitors (SSRIs), for example, were found to increase the risk of suicide, especially, in younger age groups, even when adjusting for risks caused by the illness itself (FDA review). We seem to be faced with a similar situation in relation to the use of anti-epileptic drugs (AEDs). The latter is evident in a large FDA meta-analysis that has shown AEDs to increase the risk of suicide in three different groups of patients: patients with epilepsy, patients with psychiatric disorders (e.g. depression), and patients with other disorders (e.g. chronic pain) [2]. Naturally, this is a complex area considering that all three groups of patients do potentially have a higher risk of depression and suicide! Notwithstanding the finding that patients with psychiatric disorders on AEDs were found to have a lower suicide risk, when compared to patients with epilepsy on AEDs, is interesting. The latter observation is consistent with three more studies, all of which have failed to detect an increase in suicide rates in patients with bipolar affective disorder receiving AEDs, but paradoxically, an increased rate in patients with epilepsy receiving AEDs [3-5]. The FDA finding that suicide risk is higher in the AED arm of patients with epilepsy when compared to the AED arm of psychiatrically ill patients, from the outset sounds almost counter-intuitive. However, the mystery is amenable to understanding if we entertain the possibility that in some patients, the risk of suicide can directly be affected by seizure activity (increased or decreased) irrespective of the underlying psychopathological ailments (e.g. depression).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".