<p>Incidence and Risk Factors for Suicide Attempts in Patients Diagnosed with Major Depressive Disorder</p>
Bibliographic record
Abstract
OBJECTIVE: This study seeks to investigate the cumulative incidence and risk factors of suicide attempts in an outpatient sample of adults diagnosed with major depressive disorder (MDD). MATERIALS AND METHODS: This is a longitudinal study with 377 patients aged between 18 and 60 years. Those were diagnosed with MDD with no history of suicide attempts when they sought care at the Mental Health Outpatient Clinic of the Catholic University of Pelotas and evaluated again 3 years after. Participants were evaluated with the Mini International Neuropsychiatric Interview (MINI Plus) and answered instruments of clinical investigation and a sociodemographic questionnaire. RESULTS: The cumulative incidence of suicide attempts in the sample was 10.1%. Youths aged up to 29 years (OR 2.23; 95% CI 1.13 to 4.64), with low schooling (OR 2.35; 95% CI 1.15 to 4.80), who suffered intense physical abuse during childhood (OR 2.77; 95% CI 1.31 to 5.84) and were at prior suicide risk (OR 3.39; 95% CI 1.56 to 7.37) were more likely to attempt suicide. CONCLUSION: The findings of this study may help health professionals identify depressed patients at greater risk for a first suicide attempt, supporting clinical decision and therapeutic planning.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".