Comparative Safety of Antidepressant Agents for Children and Adolescents Regarding Suicidal Acts
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the risk of suicide attempts and suicides after initiation of antidepressant medication use by children and adolescents, for individual agents. METHODS: We conducted a 9-year cohort study by using population-wide data from British Columbia. We identified new users of antidepressants who were 10 to 18 years of age with a recorded diagnosis of depression. Study outcomes were hospitalization attributable to intentional self-harm and suicide death. RESULTS: Of 20,906 children who initiated antidepressant therapy, 16,774 (80%) had no previous antidepressant use. During the first year of use, we observed 266 attempted and 3 completed suicides, which yielded an event rate of 27.04 suicidal acts per 1000 person-years (95% confidence interval [CI]: 23.9-30.5 suicidal acts per 1000 person-years). There were no meaningful differences in the rate ratios (RRs) comparing fluoxetine with citalopram (RR: 0.97 [95% CI: 0.54-1.76]), fluvoxamine (RR: 1.05 [95% CI: 0.46-2.43]), paroxetine (RR: 0.80 [95% CI: 0.47-1.37]), and sertraline (RR: 1.02 [95% CI: 0.56-1.84]). Tricyclic agents showed risks similar to those of selective serotonin reuptake inhibitors (RR: 0.92 [95% CI: 0.43-2.00]). CONCLUSION: Our finding of equal event rates among antidepressant agents supports the decision of the Food and Drug Administration to include all antidepressants in the black box warning regarding potentially increased suicidality risk for children and adolescents beginning use of antidepressants.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".