Speed of Improvement in Symptoms of Depression With Desvenlafaxine 50 mg and 100 mg Compared With Placebo in Patients With Major Depressive Disorder
Bibliographic record
Abstract
PURPOSE/BACKGROUND: This post hoc analysis examined the time point at which clinically significant improvement in major depressive disorder (MDD) symptoms occurs with desvenlafaxine versus placebo. METHODS: Data were pooled from 9 short-term, double-blind, placebo-controlled studies in adults with MDD randomly assigned to desvenlafaxine 50 mg/d, 100 mg/d, or placebo. A mixed-effects model for repeated-measures analysis of change from baseline score was used to determine the time point at which desvenlafaxine treatment groups separated from placebo on the 17-item Hamilton Rating Scale for Depression and psychosocial outcomes. The association between early improvement and week 8 outcomes was examined using logistic regression analyses. Time to remission for patients with early improvement versus without early improvement was assessed using Kaplan-Meier techniques. Comparisons between groups were performed with log-rank tests. RESULTS: In the intent-to-treat population (N = 4279 patients: desvenlafaxine 50 mg/d, n = 1714; desvenlafaxine 100 mg/d, n = 870; placebo, n = 1695), a statistically significant improvement on the 17-item Hamilton Rating Scale for Depression was observed with desvenlafaxine 50 mg/d at week 1 (P = 0.0129) and with desvenlafaxine 100 mg/d at week 2 (P = 0.0002) versus placebo. Early improvement was a significant predictor of later remission. Treatment assignment, baseline depression scale scores, and race were significantly associated with probability of early improvement. On several measures of depressive symptoms and function, desvenlafaxine 50 mg/d and 100 mg/d separated from placebo as early as week 1 and no later than week 4 in patients with MDD. IMPLICATIONS/CONCLUSIONS: These findings suggest that clinicians may be able to use depression rating scale scores early in treatment as a guide to inform treatment optimization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".