Effects of desvenlafaxine versus placebo on MDD symptom clusters: A pooled analysis
Bibliographic record
Abstract
Background: Major depressive disorder is characterized by the presence of at least five of nine specific symptoms that contribute to clinically significant functional impairment. This analysis examined the effect of desvenlafaxine (50 or 100 mg) versus placebo on symptom cluster scores and the association between early improvement in symptom clusters and symptomatic or functional remission at week 8. Methods: Using data from nine double-blind, placebo-controlled studies of desvenlafaxine for the treatment of major depressive disorder ( N=4317), the effect of desvenlafaxine 50 or 100 mg versus placebo on scores for symptom clusters based on 17-item Hamilton Rating Scale for Depression items was assessed using analysis of covariance. Association between early improvement in symptom clusters (⩾20% improvement from baseline at week 2) and symptomatic and functional remission (17-item Hamilton Rating Scale for Depression total score ⩽7; Sheehan Disability Scale score <7) at week 8 was analyzed using logistic regression. Symptom clusters based on Montgomery–Åsberg Depression Rating Scale were also examined. Results: Desvenlafaxine 50 or 100 mg was associated with significant improvement from baseline compared to placebo for all symptom clusters ( p<0.001), except a sleep cluster for desvenlafaxine 100 mg. For all symptom clusters, early improvement was significantly associated with achievement of symptomatic and functional remission at week 8 for all treatment groups ( p⩽0.0254). Conclusion: Early improvement in symptom clusters significantly predicts symptomatic or functional remission at week 8 in patients with depression receiving desvenlafaxine (50 or 100 mg) or placebo. Importantly, patients without early improvement were less likely to remit.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".