Impact of antidepressants on the risk of suicide in patients with depression in real-life conditions: a decision analysis model
Bibliographic record
Abstract
BACKGROUND: The impact of antidepressant drug treatment (ADT) on the risk of suicide is uncertain. The aim of this study was to determine in a real-life setting whether ADT is associated with an increased or a reduced risk of suicide compared to absence of ADT (no-ADT) in patients with depression. METHOD: A decision analysis method was used to estimate the number of suicides prevented or induced by ADT in children and adolescents (10-19 years old), adults (20-64 years old) and the elderly (65 years) diagnosed with major depression. The impact of gender and parasuicide history on the findings was explored within each age group. Sensitivity analyses were used to assess the robustness of the models. RESULTS: Prescribing ADT to all patients diagnosed with depression would prevent more than one out of three suicide deaths compared to the no-ADT strategy, irrespective of age, gender or parasuicide history. Sensitivity analyses showed that persistence in taking ADT would be the main characteristic influencing the effectiveness of ADT on suicide risk. CONCLUSIONS: Public health decisions that contribute directly or indirectly to reducing the number of patients with depression who are effectively administered ADT may paradoxically induce a rise in the number of suicides.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".