Does anxiety moderate the effectiveness of mirtazapine in patients with treatment-resistant depression? A secondary analysis of the MIR trial
Bibliographic record
Abstract
BACKGROUND: There is a lack of evidence to guide treatment of comorbid depression and anxiety. Preliminary evidence suggests mirtazapine may be effective in treating patients with both depression and anxiety symptoms. METHODS: We undertook a secondary analysis of mirtazapine (MIR): a placebo-controlled trial of the addition of mirtazapine to a selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor in treatment-resistant depression (TRD) in primary care. We subdivided participants into three groups by baseline generalized anxiety disorder score (GAD-7): severe (GAD-7 ⩾ 16), moderate (GAD-7 = 11-15), no/mild (GAD-7 ⩽ 10). We used linear regression including likelihood-ratio testing of interaction terms to assess how baseline anxiety altered the response of participants to mirtazapine as measured by 12-week GAD-7 and Beck Depression Inventory II (BDI-II) scores. RESULTS: = 0.088) at 12 weeks. Participants with severe generalized anxiety receiving mirtazapine had lower 12-week GAD-7 score (adjusted difference between means (ADM) -2.82, 95% confidence interval (CI) -0.69 to -4.95) and larger decreases in BDI-II score (ADM -6.36, 95% CI -1.60 to -10.84) than placebo. Conversely, there was no anxiolytic benefit (ADM 0.28, 95% CI -1.05 to 1.60) or antidepressant benefit (ADM -0.17, 95% CI -3.02 to 2.68) compared with placebo in those with no/mild generalized anxiety. CONCLUSIONS: These findings extend the evidence for the effectiveness of mirtazapine to reduce generalized anxiety in TRD in primary care. These results may inform targeted prescribing in depression based on concurrent anxiety symptoms, although these conclusions are constrained by the post-hoc nature of this analysis.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".