Duloxetine for major depressive episodes in the course of psychotic disorders: an observational clinical trial
Bibliographic record
Abstract
Patients with psychotic disorders often suffer from intercurrent major depressive episodes (MDE). Case reports suggested successful antidepressive treatment with duloxetine, a selective dual reuptake inhibitor of serotonin and norepinephrine. We initiated this open prospective clinical trial to evaluate efficacy, safety and tolerability of this approach. Patients with a psychotic lifetime diagnosis suffering from mildly severe MDE were treated with duloxetine over a period of 6 weeks. We evaluated effects on mood, monitored the psychotic psychopathology and assessed side effects, basal clinical and pharmacological parameters. Twenty patients were included and experienced a significant improvement of their MDE during the observation period (Calgary Depression Scale for Schizophrenia and Hamilton Depression Scale). Psychotic positive symptoms remained stably absent, while negative syndrome and global psychopathology considerably improved (Positive and Negative Syndrome Scale). In general, the treatment was well tolerated, serum prolactin levels stayed unchanged, but pharmacokinetic interactions with a number of antipsychotic agents were observed. This open prospective evaluation showed antidepressive efficacy of duloxetine in patients with co-morbid psychotic disorders. With regard to the psychotic disorder, the treatment appears to be safe and well tolerable. Further investigations should involve a randomized control group.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".