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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".