PS100. Open-label Desvenlafaxine as Monotherapy for Persistent Depression Disorder: A Pilot Study
Bibliographic record
Abstract
Abstract Objective: Chronic depression is common and associated with significant functional impairment (Haykal & Akiskal, 1999). Desvenlafaxine is effective in the treatment of major depression (Thase et al., 2009), yet there is no data on its efficacy in the treatment of persistent depressive disorder. The aim of this study was to investigate the efficacy, safety and tolerability of desvenlafaxine monotherapy in patients with persistent depressive disorder. Methods: Thirty-five patients with persistent depressive disorder consented to participate in an open-label 8-week trial of monotherapy with desvenlafaxine. Primary efficacy measures were administered at baseline, weeks 1, 2, 4, and week 8.The dose of desvenlafaxine was fixed (50mg/day) until week 4, after which it was flexible up to 100mg/day, based on response and tolerability. Results: Montgomery Asberg Depression Scale scores significantly decreased from baseline (M=23.61, SD=5.51) to end of treatment (M=12.29, SD=8.41), p<.0001. Severity of illness, as measured by the Clinical Global Impression scale, as well as self-reported depressive symptom scores, significantly decreased from baseline to end of treatment (p<.0001). Improvement in quality of life (p<.0001), levels of perceived stress (p<.0001), coping styles (p<.0001), and work impairment (p<.01) were noted over the course of treatment. Conclusions: Overall results indicate that desvenlafaxine is effective in reducing depressive symptoms and improving functioning in patients with persistent depressive disorder. Further, results provide evidence of good safety and tolerability of desvenlafaxine in this population. These results support the further investigation of desvenlafaxine for this condition using larger, placebo controlled, randomized control trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".