Bibliographic record
Abstract
OBJECTIVE: About one-half to two-thirds of all suicides are by people who suffer from mood disorders; preventing suicides among those who suffer from them is thus central for suicide prevention. Understanding factors underlying suicide risk is necessary for rational preventive decisions. METHOD: The literature on risk factors for completed and attempted suicide among subjects with depressive and bipolar disorders (BDs) was reviewed. RESULTS: Lifetime risk of completed suicide among psychiatric patients with mood disorders is likely between 5% and 6%, with BDs, and possibly somewhat higher risk than patients with major depressive disorder. Longitudinal and psychological autopsy studies indicate suicidal acts usually take place during major depressive episodes (MDEs) or mixed illness episodes. Incidence of suicide attempts is about 20- to 40-fold, compared with euthymia, during these episodes, and duration of these high-risk states is therefore an important determinant of overall risk. Substance use and cluster B personality disorders also markedly increase risk of suicidal acts during mood episodes. Other major risk factors include hopelessness and presence of impulsive-aggressive traits. Both childhood adversity and recent adverse life events are likely to increase risk of suicide attempts, and suicidal acts are predicted by poor perceived social support. Understanding suicidal thinking and decision making is necessary for advancing treatment and prevention. CONCLUSION: Among subjects with mood disorders, suicidal acts usually occur during MDEs or mixed episodes concurrent with comorbid disorders. Nevertheless, illness factors can only in part explain suicidal behaviour. Illness factors, difficulty controlling impulsive and aggressive responses, plus predisposing early exposures and life situations result in a process of suicidal thinking, planning, and acts.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".