The Influence of Baseline Severity on Efficacy of Escitalopram and Citalopram in the Treatment of Major Depressive Disorder: An Extended Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the differences between escitalopram and citalopram in the treatment of patients with major depressive disorder across a range of baseline severity of depression using trend analysis. METHODS: Data from the three placebo-controlled studies comparing escitalopram to citalopram were analyzed. The pre-specified primary outcome variable was MADRS total score; secondary outcomes included Clinical Global Impression-Severity (CGI-S) and -Improvement (CGI-I) scores. All analyses were based on an intent-to-treat (ITT) population and all direct comparisons were done by ANCOVA adjusting for baseline value and centre. RESULTS: Analyses of the pooled data (N=1203) show that, while the difference between citalopram and placebo was approximately constant across the range of baseline severity, the difference between escitalopram and placebo (p=0.0010 for no trend) and between escitalopram and citalopram (p=0.0012 for no trend) became greater, the more severely depressed the patients were at baseline. A similar pattern was apparent with the CGI-S and CGI-I results. There was a significant superiority of escitalopram over citalopram in response rate (defined as > or = 50% decrease in MADRS total score), and this difference increased with increasing baseline severity. CONCLUSION: These trend analyses thus indicate that the superiority of escitalopram over citalopram is more apparent as the baseline severity of depression increases.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".