Anxiety As a Predictor of Treatment Outcome in Children and Adolescents with Depression
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Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the impact of co-morbid illnesses on treatment outcomes in depressed children and adolescents aged 7-17 who were treated with fluoxetine. METHOD: This data set was drawn from two large clinical trials involving children and adolescents with depression. Subjects with a diagnosis of major depressive disorder and depressive symptoms of at least moderate severity as defined by a Children's Depression Rating Score, Revised (CDRS-R) total score >or=40 and a Clinical Global Impressions-Severity (CGI-S) rating >or=4 were included. Subjects were randomized to receive fluoxetine or placebo over an 8-week period. Predictor analyses examining two primary outcomes were conducted: (1) Response based on Clinical Global Impressions-Improvement (CGI-I) score of 1 or 2, and (2) remission based on CDRS-R score of <or=28. Logistic regression models were run to assess whether anxiety disorders were a predictor of response or remission. RESULT: A total of 309 study participants were included. The only factor found to influence response was treatment with fluoxetine (p = 0.022, odds ratio [OR] = 2.08, 95% confidence interval [CI] 1.30, 3.31). Several factors were found to influence remission: Treatment with fluoxetine (p < 0.0001, OR = 3.17, 95% CI 1.80, 5.57), gender (p = 0.024, OR = 1.90, 95% CI 1.09, 3.30), and number of co-morbid diagnoses (p = 0.026, OR 0.73, 95% CI 0.55, 0.96). CONCLUSION: Anxiety disorders alone did not predict response or remission, but the total number of co-morbid illnesses was associated with remission in depressed children and adolescents treated with fluoxetine.
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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 | 0.001 |
| 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 it