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Record W2030334791 · doi:10.3109/10398561003681319

Are Adolescents Dying by Suicide Taking SSRI Antidepressants? A Review of Observational Studies

2010· review· en· W2030334791 on OpenAlexfundno aff
Michael Dudley, Robert D. Goldney, Dušan Pavlović

Bibliographic record

VenueAustralasian Psychiatry · 2010
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersCanadian Medical Association
KeywordsObservational studyPsychiatryDepression (economics)MedicineSuicide preventionSuicide attemptPopulationInjury preventionPoison controlMedical emergencyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the association between adolescents who die by suicide and their use of SSRI antidepressants. METHOD: We sought all available observational studies of individual adolescent suicides that were population based and which contained individual data on SSRIs at or around the time of death. RESULTS: From an initial database of 656 studies, we identified and examined six studies. In the latter, nine of 574 young people (1.6%) who died by suicide had had recent exposure to SSRIs. CONCLUSION: The rarity of SSRI usage prior to adolescent suicide is not supportive of the assertion that SSRIs are associated with increased suicide in young people. Given the prevalence of depression associated with youth suicide, it favours the conclusion that most adolescents dying by suicide have not had the potential benefit of antidepressants at the time of their deaths. This finding should allow practitioners, with appropriate precautions and as part of a comprehensive management plan, to more confidently prescribe SSRIs for young people with moderate to severe clinical 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.008
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.191
GPT teacher head0.448
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations33
Published2010
Admission routes1
Has abstractyes

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