Combining Bupropion SR With Venlafaxine, Paroxetine, or Fluoxetine
Bibliographic record
Abstract
BACKGROUND: This study was designed to evaluate the effect of combining bupropion sustained release (SR) with venlafaxine, paroxetine, or fluoxetine in patients who reported unacceptable sexual dysfunction when treated with monotherapy with the latter 3 agents. METHOD: Following a minimum of 6 weeks of antidepressant treatment with a selective serotonin reuptake inhibitor (SSRI) or venlafaxine (a serotonin-norepinephrine reuptake inhibitor), eligible subjects received a further 8 weeks of monitored combination therapy with bupropion SR at a dose of 150 mg/day with no alterations to index antidepressant dosing. RESULTS: There was a clinically significant benefit in 14 (78%) of 18 partial responders or nonresponders, and 33% (N = 6) achieved a full response (chi2= 8.06, df = 2, p = .017). Sexual dysfunction, particularly a decrease in orgasmic delay, was also significantly improved with combination therapy (men: paired t = -2.1, df = 6, p = .08; women: paired t = -3.0, df = 7, p = .02). Plasma monitoring of drugs and their metabolites revealed a statistically significant increase in venlafaxine levels (F = 6.89, df = 4,24; p = .001) accompanied by a decrease in O-desmethylvenlafaxine (F = 14.26; df = 4,24; p < .0005) during combined treatment with bupropion SR. There were no statistically significant changes in plasma levels of SSRIs (paroxetine and fluoxetine) during the trial. CONCLUSION: Bupropion had an effect on the pharmacokinetics of venlafaxine but not those of the SSRIs. Further investigation of combination treatments under randomized, double-blind conditions is recommended.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".