Citalopram and Bupropion-SR
Bibliographic record
Abstract
OBJECTIVE: There are limited data comparing medication strategies in patients with treatment-resistant depression. In this study, we compared the effects of combining citalopram and bupropion-SR versus switching to the other monotherapy in treatment-resistant depression. METHOD: This was a naturalistic, open-label cohort study. Patients with DSM-IV major depressive disorder who had not responded to at least 1 previous antidepressant and at least 6 weeks of treatment with citalopram or bupropion-SR were treated in a standard clinical protocol. In alternate months, eligible consecutive patients were treated by adding citalopram or bupropion-SR, or by switching to the other medication. Patients were assessed at baseline and after 6 weeks of treatment with the 29-item version of the Structured Interview Guide for the Hamilton Depression Rating Scale, Seasonal Affective Disorders Version (SIGH-SAD). RESULTS: A total of 61 patients completed the study: 32 in the combination condition and 29 in the monotherapy switch condition. The combination condition was superior to the monotherapy switch in the SIGH-SAD change score (-14.8 vs. -10.1, respectively, p <.04) and the proportion of patients in clinical remission (28% vs. 7%, p <.05). There were no differences in the proportion of patients who had side effects or in the severity of the side effects experienced. CONCLUSION: The results of this cohort study suggest that combining citalopram and bupropion-SR is more effective than switching to a monotherapy. Combination treatment was well tolerated with no greater side effect burden than monotherapy. Limitations of this study include the nonrandomized design, open-label treatment, and small sample size.
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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 | 0.002 |
| 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) | 0.004 | 0.001 |
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".