Treating Suicidality in Depressive Illness. Part 2: Does Treatment Cure or Cause Suicidality?
Bibliographic record
Abstract
Objectives: To systematically review studies of treatment efficacy for suicidality in mood disorders. To consider the evidence for whether antidepressants may induce suicidality. Method: Systematic review of the literature. Results and Conclusions: There is fairly good evidence that lithium reduces completed suicide and attempt rates in people with bipolar disorder and recurrent unipolar depression. Antidepressants and psychological treatments may reduce suicidal ideation in depressed patients. Antidepressant trials do not, however, a priori target suicidality as an outcome, and inferences made are post hoc. For practical reasons, no adequate trials to date have tested the efficacy of treatment aimed at reducing completed suicide in people with depressive disorders. Antidepressants have been implicated in suicide in one metaanalysis (the elderly) and in one case-control study (youth), signalling the need for caution. However, most metaanalyses have found no significant excess of completed suicide among antidepressant users, compared with placebo groups, in adults and juveniles, but excess nonfatal suicidality is found more often in children and adolescents who take antidepressants (except fluoxetine). The controversy is ongoing. (Reprinted with permission from the Canadian Journal of Psychiatry, 2007; 52 (6 Suppl 1): 85S–191S; full text of the article available online at http://publications.cpa-apc.org/media.php?mid=425)
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".