Clinical correlates of new-onset and persistent suicidal ideation in adolescents with major depressive disorder
Bibliographic record
Abstract
Background Suicidal ideation (SI) is common in adolescents with major depressive disorder (MDD). SI not only poses a serious threat to the patient’s life safety, but also significantly hinders the process of psychological recovery and the restoration of social functioning. However, there is still a relative lack of longitudinal studies on the factors influencing SI in adolescents with MDD. Therefore, this study aimed to explore the longitudinal trajectory of SI in adolescents with MDD and to identify the relevant influencing factors. Methods This study included 122 adolescents with MDD. At baseline and one-year follow-up, patients were assessed for SI. Based on the assessment results, patients were divided into SI group and non-SI group. In addition, the standardized questions and the Center for Epidemiological Studies Depression scale (CES-D), the Childhood Trauma Questionnaire (CTQ), and the Toronto Alexithymia Scale (TAS-20) were used to evaluate non-suicidal self-injury (NSSI), depressive symptoms, childhood maltreatment (CM), and alexithymia. Logistic stepwise regression analyses were employed to identify factors independently associated with SI in adolescents with MDD. Results In adolescents with MDD, the prevalence of SI was 68.0%. At the follow-up period, the prevalence of persistent suicidal ideation (PSI) was 19.7%, and the prevalence of new-onset SI was 20.5%. Regression analyses showed that single-child family (OR = 3.969, 95%CI: 1.227 - 12.839, P = 0.021), TAS-20 score (OR = 1.091, 95%CI: 1.006 - 1.184, P = 0.035), and difficulties identifying feelings (OR = 1.134, 95%CI: 1.000 - 1.287, P = 0.050) were risk factors for PSI. NSSI (OR = 4.552, 95%CI: 1.488 - 13.921, P = 0.008) and positive affect (OR = 1.424, 95%CI: 1.125-1.804, P = 0.003) were risk factors for new-onset SI. Conclusion Adolescents with MDD have a high risk of PSI, and new-onset SI should not be ignored. Factors such as single-child family, alexithymia, NSSI, and reduction of positive affect significantly affect the occurrence and persistence of SI. Therefore, early intervention targeting these factors is important to reduce the risk of adolescent suicide and improve mental health outcomes.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".