Cognitive Vulnerability to Depression in Adolescents with Depression, their Healthy Siblings and a Control Group: A Cross-sectional Study
Bibliographic record
Abstract
Introduction At least half of first depressive episode appear before adulthood. A negative cognitive bias is present among individuals who suffer from major depression. This bias is also reported among individuals at high risk of major depression (e.g. child of depressed mother). When present, cognitive vulnerability may predispose to major depression. No study to date aimed to evaluate the cognitive vulnerability of siblings of depressed individuals. Objectives and aims To review the principles behind cognitive vulnerability. To assess cognitive vulnerability in depressed adolescents, in healthy siblings and in a control group. Methods Eighty adolescents (27 adolescents treated for depression, 24 healthy siblings and 29 controls), aged between 12 and 20 years old, were recruited and assessed using validated measures of bio-psycho-social vulnerabilities. All diagnoses were confirmed using a K-SADS interview. Cortisol level samples were obtained through morning saliva. Cognitive vulnerability was assessed using self-report questionnaires (CES-D, LEIDS-R, EPQ) as well as computer-based tasks (Ekman's tasks of facial recognition and the movie for assessment of social cognition [MASC]). We translated the MASC from German to French. The parents of the adolescents also filled the LEIDSR and the CESD. Results The LEIDS-R presented a significant increase in certain subscales (hopelessness, aggression and rumination) compared to the healthy siblings and the controls. Interestingly, there was also a correlation between the LEIDS R results of the parents and of the depressed adolescent ( r = 0.43, P = 0.04). Conclusions The LEIDSR appears to be the most sensitive task to detect cognitive vulnerability. A relation between the parent response and the depressed adolescent response could be found. Disclosure of interest The authors have not supplied their declaration of competing interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".