Quais são os recentes achados clínicos sobre a associação entre depressão e suicídio?
Bibliographic record
Abstract
OBJECTIVE: Suicide is one of the leading causes of mortality worldwide, especially among young subjects. Suicide is considered the outcome of a multidimensional and complex phenomenon, which is a result of the interaction between several factors. The association between psychopathology and suicide has been extensively investigated. Major depression plays an important role among the psychiatric diagnoses associated with suicide. This finding seems to be confirmed by different study designs, and in distinct populations. The present paper aims to briefly review the recent findings regarding the suicide-related clinical features of depression. Moreover, strategies for suicide prevention were also reviewed. REVIEW: Recent references were identified and grouped in order to illustrate the main contributions about depression and suicide. Briefly, the literature review stresses the high prevalence of major depression among subjects presenting suicide behaviors. Psychopathological traits, such as aggression and impulsivity play a relevant role in triggering suicidal behaviors. Strategies for suicide prevention were also reviewed in Brazil and internationally. In general, detection and treatment are effective in reducing suicide rates. CONCLUSION: Studies regarding suicide behaviors have had a pragmatic approach, and generated a large body of evidence about correlates of suicide. However, these studies have not been able to provide a consistent theoretical explanation for this phenomenon. The recent adoption of modern strategies represents a possibility of enhancing the research capability of such studies. In order to be clinically useful, findings should make it possible to deepen the understanding over the experience of a suicidal person, as well as to design specific strategies for prevention and treatment in population subgroups.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".