Sequential Treatment With Fluoxetine and Relapse-Prevention CBT to Improve Outcomes in Pediatric Depression
Bibliographic record
Abstract
OBJECTIVE: The authors evaluated a sequential treatment strategy of fluoxetine and relapse-prevention cognitive-behavioral therapy (CBT) to determine effects on remission and relapse in youths with major depressive disorder. METHOD: Youths 8-17 years of age with major depression were treated openly with fluoxetine for 6 weeks. Those with an adequate response (defined as a reduction of 50% or more on the Children's Depression Rating Scale-Revised [CDRS-R]) were randomly assigned to receive continued medication management alone or continued medication management plus CBT for an additional 6 months. The CBT was modified to address residual symptoms and was supplemented by well-being therapy. Primary outcome measures were time to remission (with remission defined as a CDRS-R score of 28 or less) and rate of relapse (with relapse defined as either a CDRS-R score of 40 or more with a history of 2 weeks of symptom worsening, or clinical deterioration). RESULTS: Of the 200 participants enrolled in acute-phase treatment, 144 were assigned to continuation treatment with medication management alone (N=69) or medication management plus CBT (N=75). During the 30-week continuation treatment period, time to remission did not differ significantly between treatment groups (hazard ratio=1.26, 95% CI=0.87, 1.82). However, the medication management plus CBT group had a significantly lower risk of relapse than the medication management only group (hazard ratio=0.31, 95% CI=0.13, 0.75). The estimated probability of relapse by week 30 was lower with medication management plus CBT than with medication management only (9% compared with 26.5%). CONCLUSIONS: Continuation-phase relapse-prevention CBT was effective in reducing the risk of relapse but not in accelerating time to remission in children and adolescents with major depressive disorder.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".