Assessing the efficacy of 2 years of maintenance treatment with venlafaxine extended release 75–225 mg/day in patients with recurrent major depression: a secondary analysis of data from the PREVENT study
Bibliographic record
Abstract
The objective of this study was to evaluate the long-term efficacy of venlafaxine extended release (ER) < or =225 mg/day in patients with recurrent major depressive disorder (MDD). In this double-blind trial, outpatients with recurrent MDD (N=1096) were randomized to 10 weeks of acute-phase treatment with venlafaxine ER (75-300 mg/day) or fluoxetine (20-60 mg/day) followed by a 6-month continuation phase and two consecutive 12-month maintenance phases. At the start of each maintenance period, venlafaxine ER responders were randomized to double-blind venlafaxine ER or placebo. In this analysis, data from responders to acute and continuation treatment were analyzed during the combined maintenance phases while receiving venlafaxine ER < or =225 mg/day. Failure to maintain response was defined as an increase in maintenance dose to 300 mg/day or recurrence. Differences were calculated using Kaplan-Meier methods and compared using log-rank tests. Continuation-phase responders (n=114) receiving venlafaxine ER < or =225 mg/day comprised the analysis population (venlafaxine ER: n=55; placebo: n=59). The estimated probability for remaining well across 24 months of maintenance treatment was 67% for venlafaxine ER and 41% for placebo (P=0.007). Venlafaxine ER effectively maintained response at doses < or =225 mg/day for up to 2.5 years in patients with recurrent MDD. The findings are consistent with those of the full data set.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".