Integrating Psychometric and Neurocognitive Biomarkers in Computational Models to Predict Cognitive Behavioral Therapy Outcomes in Adolescents with Anxiety and Depression
Bibliographic record
Abstract
This study investigated the predictive relationships between psychometric indicators and neurocognitive biomarkers in determining Cognitive Behavioral Therapy (CBT) outcomes among adolescents diagnosed with anxiety and depressive disorders. A quantitative prospective longitudinal research design was employed to examine how psychological and cognitive factors contributed to variability in treatment response. The study sample consisted of 120 adolescents aged between 12 and 18 years who were receiving structured CBT interventions in outpatient clinical settings. Data were collected at pretreatment, midpoint, and posttreatment stages using standardized psychometric scales and neurocognitive assessment tasks. Psychometric variables included anxiety severity, depressive symptoms, cognitive distortions, behavioral avoidance, emotional regulation difficulties, and resilience, while neurocognitive variables included attention bias, executive control, working memory performance, cognitive flexibility, emotional reactivity, and reward sensitivity. Descriptive analysis indicated substantial reductions in symptom severity over the course of treatment, with mean anxiety scores decreasing from 31.42 (SD = 6.85) at pretreatment to 17.63 (SD = 5.27) at posttreatment, while depressive symptoms declined from 28.73 (SD = 7.11) to 16.84 (SD = 5.89). Approximately 65% of participants were classified as treatment responders, demonstrating clinically significant improvement following CBT. Correlation analysis revealed significant relationships between psychological variables and treatment outcomes. Behavioral avoidance (r = -0.37, p < 0.01) and emotional regulation difficulties (r = -0.33, p < 0.01) were negatively associated with treatment improvement, whereas resilience demonstrated a positive correlation with treatment outcomes (r = 0.36, p < 0.01). Hierarchical regression analysis indicated that psychometric variables explained 39% of the variance in CBT outcomes (R² = 0.39, p < 0.001). When neurocognitive predictors were incorporated, the explanatory power of the model increased to 52% of the variance (R² = 0.52, p < 0.001). Executive control (β = 0.34, p < 0.01) and cognitive flexibility (β = 0.28, p < 0.05) emerged as significant positive predictors of treatment improvement, while emotional reactivity (β = -0.22, p < 0.05) was negatively associated with therapy outcomes. These findings demonstrated that integrating psychometric and neurocognitive indicators improved the prediction of CBT effectiveness. The study highlighted the importance of considering cognitive functioning, emotional regulation capacity, and behavioral coping mechanisms when evaluating treatment outcomes in adolescent psychotherapy.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".