An analysis of correlations among four outcome scales employed in clinical trials of patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: The 17-item Hamilton Depression Rating Scale (HAM-D 17) remains the 'gold standard' for measuring treatment outcomes in clinical trials of depressed patients. The Montgomery Asberg Depression Rating Scale (MADRS), Clinical Global Impressions-Severity (CGI-S) and -Improvement (CGI-I) scales are also widely used. OBJECTIVE: This analysis of data from 22 double-blind, placebo-controlled clinical studies of venlafaxine in adult patients with major depressive disorder was aimed at assessing correlations among these 4 scales. METHODS: Changes from baseline for MADRS, HAM-D 17 and CGI-S, and end point CGI-I scores and response (>or=50% decrease from baseline HAM-D 17 or MADRS, or CGI-S or CGI-I score <or=2) were analysed. Pearson correlation coefficients were calculated for all pairs of the four scales (HAM-D 17/MADRS, HAM-D 17/CGI-S, HAM-D 17/CGI-I, MADRS/CGI-S, MADRS/CGI-I, CGI-S/CGI-I) at different time points. Effect sizes were calculated using the Cohen d. RESULTS: Correlations were significant at all time points (p < 0.0001), increased over the course of treatment, and were similar across treatment groups. Effect sizes ranged from 0.31 to 0.42; MADRS and CGI-I effect sizes were slightly greater compared with HAM-D 17 or CGI-S for continuous measures and response. CONCLUSION: Although MADRS and CGI-I were more sensitive to treatment effects, HAM-D 17, MADRS, CGI-S and CGI-I scores present a consistent picture of response to venlafaxine treatment.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".