Do selective serotonin reuptake inhibitors cause suicide?: Let's keep it in perspective
Bibliographic record
Abstract
Do selective serotonin reuptake inhibitors cause suicide? Risk of suicide should be assessed for whole class of antidepressantsEditor-Gunnell et al's report on suicide risk with selective serotonin reuptake inhibitors (SSRIs) raises several issues. 1 Firstly, clinicians have observed that the first weeks of treatment of severe depression with an antidepressant are accompanied by a higher risk of suicide because of a drug induced motor disinhibition that is not yet accompanied by mood improvement. 2 Secondly, the authors' finding of a trend towards a protective effect of SSRIs against suicidal thoughts (odds ratio 0.77) compared with a trend towards an increased risk of self harm (odds ratio 1.57) is paradoxical.More surprising is the heterogeneity of results among SSRIs.Why would sertraline show a protective effect for suicidal thoughts and simultaneously increase the risk of self harm?The risk difference between citalopram and its active S-enantiomere, escitalopram, is also strange.No strong biological rationale can explain such heterogeneity among drugs with the same mechanism of action.Thirdly, the authors mention that the Medicine and Healthcare products Regulatory Agency found little evidence for a risk difference between SSRIs and the other antidepressants.The two accompanying papers show that the suicidal risk seems similar for serotoninergic and tricyclic antidepressants. 3 4The risk of suicide must be assessed for the whole class of antidepressants.The next stage would be to measure the risk of suicide according to the time since starting an antidepressant.Initially, the risks are higher than the benefits.To confirm old clinical observations by evidence based methods would be interesting and useful.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".