Risk of suicidal behavior in patients with major depression and bipolar disorder – A systematic review and meta-analysis of registry-based studies
Bibliographic record
Abstract
Suicide is a health priority and one of the most common causes of death in mood disorders. One of the limitations of this type of research is that studies often establish rates of suicide behaviors in mood disorders by using diverse comparison groups or simply monitoring cohort of patients over a time period. In this registry-based systematic review, national registers were identified through searches in six academic databases, and information about the occurrence of suicide behaviors in mood disorders was systematically extracted. Odds ratios were subsequently calculated comparing rates of death by suicide in mood disorders in comparison with age and period matched rates of death by suicide in the general population obtained from country-wide national registers. The aim was to provide the most recent summary of epidemiological and clinical factors associated to suicide in mood disorders whilst calculating the likelihood of death by suicide in mood disorders in comparison with non-affected individuals according to national databases. The study follows the Preferred Reporting Guidelines for Systematic Reviews and Meta-analyses and was prespecify registered on Prospero (CRD42020186857). Results suggest that patients with mood disorders are at substantially increased risk of attempting and dying by suicide. Several epidemiological, clinical and social factors are reported to be associated with clinical populations at risk of suicide. Meta-analyses of completed deaths by suicide suggest that the likelihood for dying by suicide in mood disorders is 8.62 times higher in major depression and 8.66 times higher in bipolar disorder with higher number of untoward events in women compared to men in both conditions. The likelihood of dying by suicide in major depressive disorders is higher in the first year following discharge. Clinical guidelines might consider longer periods of monitoring following discharge from hospital. Overall, due to the higher risk of suicide in mood disorders, efforts should be made to increase detection and prevention whilst focusing on reducing risk in the most severe forms of illness with appropriate treatment to promote response and remission at the earliest convenience.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".