Antidepressant combination versus antidepressants plus second-generation antipsychotic augmentation in treatment-resistant unipolar depression
Bibliographic record
Abstract
Patients with treatment-resistant unipolar depression (TRD) are treated with antidepressant combinations (ADs) or with second-generation antipsychotics plus AD (SGA+AD) augmentation; however, the clinical characteristics, the factors associated independently with response to SGA+AD, and the outcome trajectories have not yet been characterized. We performed a naturalistic study on the latest stable trial (medication unchanged for about 3 months) in 86 TRD patients with resistance to at least two ADs trials, who received ADs (n=36) or SGA+AD (n=50) treatments. Montgomery-Asberg Depression Rating Scale (MADRS), Hamilton-Depression Rating Scale (HAM-D17), and other scales were administered before (T0) and after the latest 3-month stable trial (T3). Compared to ADs, the SGA+AD group showed increased percentage of depression with psychotic features, comorbidity for personality disorders and substance use disorders (SUD), higher number of failed ADs pharmacotherapies and depressive symptoms at T0 on all scales (P<0.001). Compared to T0, both treatments significantly decreased depressive symptoms on MADRS and HAM-D17 at T3 (P<0.001); however, the SGA+AD augmentation produced a greater decline in mean score. Logistic regression analysis indicated that psychotic features, personality disorders, and SUD were independently associated with SGA+AD treatment. Given the greater improvement in depression following SGA+AD augmentation, SGA augmentation should be indicated as a first-line treatment in severe TRD with psychotic features, SUD, and personality disorders.
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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) | 0.002 | 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) | 0.001 | 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".