The Influence of Baseline Severity on Efficacy of Escitalopram and Citalopram in the Treatment of Major Depressive Disorder: An Extended Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it