A Pilot Study of Citalopram Treatment in Preventing Relapse of Depressive Episode after Acute Treatment.
Bibliographic record
Abstract
PURPOSE: To examine the benefit of continuation treatment with citalopram in adolescents 13 to 18 years of age with major depression using a multi-site randomized placebo controlled discontinuation design. METHODS: Subjects with depression who responded to open label treatment with citalopram in 12-week acute phase were randomized to continued treatment with citalopram or placebo for 24 weeks. RESULTS: Twenty five subjects were randomized to either continued treatment with citalopram (n = 12) versus placebo (n = 13). Seventy-five percent of subjects on citalopram (75%) remained well as compared to placebo (62%). Time to relapse was compared between groups using the log rank test and was not found to be significantly different (χ(2)(1) = 0.35, P = 0.55). A Cox proportional hazards model including drug assignment (hazard ratio (HR = 0.51, 95% CI 0.11 to 2.36, P = 0.39), gender (HR = 0.58, 95% CI 0.14 to 2.37, P = 0.44), or HAM-score at entry to continuation phase (HR = 1.33, 95% CI 0.90 to 1.95, P = 0.95) was not significant. CONCLUSION: Although we did not find statistically significant differences between citalopram and placebo, the findings suggest a possible benefit of continued treatment with citalopram over placebo. A larger clinical trial with adequate power is required to confirm or disconfirm these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".