Mirtazapine for treatment-resistant depression: a preliminary report
Bibliographic record
Abstract
OBJECTIVE: To describe the effectiveness and tolerability of mirtazapine, a noradrenergic and specific serotonergic antidepressant, in the open-label treatment of patients with depression who were resistant to other antidepressant agents. METHODS: The charts of 24 patients who met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, (DSM-IV) criteria for major depressive disorder and were treated with mirtazapine after partial or nonresponse to standard antidepressants were reviewed for clinical response. Outcome was determined by using the Clinical Global Impressions of Improvement (CGI-I) Scale. RESULTS: Symptomatic improvement was observed in 9 (38%) of 24 patients during an average of 14.1 months of mirtazapine treatment at a mean dose of 36.7 mg/day. Five (21%) patients discontinued mirtazapine because of side effects such as fatigue, weight gain and nausea. Five (21%) patients were receiving combination therapy with another antidepressant when mirtazapine treatment was initiated. CONCLUSIONS: This open-label study suggests that a subgroup of patients with treatment-resistant depression may benefit from mirtazapine treatment. Further controlled studies are required to demonstrate the efficacy of mirtazapine in treatment-resistant depression.
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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 | 0.000 |
| 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 | 0.001 | 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".