Anxiety As a Predictor of Treatment Outcome in Children and Adolescents with Depression
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".