Review: antidepressant use increases the risk of suicidal behaviour and ideation in children
Bibliographic record
Abstract
Hammad TA, Laughren T, Racoosin J. Suicidality in pediatric patients treated with antidepressant drugs. Arch Gen Psychiatry 2006;63:332–9.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Does antidepressant use affect the risk of suicidality in children? ### ![Graphic][5] Design: Systematic review with meta-analysis. ### ![Graphic][6] Data sources: Submissions to the US Food and Drug Administration (FDA). ### ![Graphic][7] Study selection and analysis: All submitted data on placebo-controlled randomised controlled trials (RCTs) of antidepressants in children were included. Only adverse events that occurred during the double blind treatment period, or within one day of its completion were included. Further data searches for potentially suicide related adverse events were requested from drug companies responsible for 23 of the RCTs. Individual patient data on all potentially suicide related adverse events were provided by drug companies. Narrative summaries of each potentially suicide-related adverse event were reviewed by independent blinded assessors who were experts in paediatric suicidality. Events were categorised as suicide attempt, preparation for impending suicidal behaviour, suicidal … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BGeneral%2BPsychiatry%26rft.stitle%253DArch%2BGen%2BPsychiatry%26rft.aulast%253DHammad%26rft.auinit1%253DT.%2BA.%26rft.volume%253D63%26rft.issue%253D3%26rft.spage%253D332%26rft.epage%253D339%26rft.atitle%253DSuicidality%2Bin%2Bpediatric%2Bpatients%2Btreated%2Bwith%2Bantidepressant%2Bdrugs.%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchpsyc.63.3.332%26rft_id%253Dinfo%253Apmid%252F16520440%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/archpsyc.63.3.332&link_type=DOI [3]: /lookup/external-ref?access_num=16520440&link_type=MED&atom=%2Febmental%2F10%2F1%2F20.atom [4]: /lookup/external-ref?access_num=000235971200013&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif
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 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".