Distinct Effects of Antidepressants in Association With Mood Stabilizers and/or Antipsychotics in Unipolar and Bipolar Depression
Bibliographic record
Abstract
PURPOSE/BACKGROUND: There is a dearth of studies comparing the clinical outcomes of patients with treatment-resistant unipolar (TRD) depression and depression in bipolar disorder (BD) despite similar treatment strategies. We aimed to evaluate the effects of the pharmacological combinations (antidepressants [AD], mood stabilizers [MS], and/or antipsychotics [AP]) used for TRD and BD at the McGill University Health Center. METHODS/PROCEDURES: We reviewed health records of 206 patients (76 TRD 130 BD) with TRD and BD treated with similar augmentation strategies including AD with MS (AD+MS) or AP (AD+AP) or combination (AD+AP+MS). Clinical outcomes were determined by comparing changes on the 17-time Hamilton Depression Rating Scale (HAMD-17), Quick Inventory of Depressive Symptomatology, and Clinical Global Impression-Severity of Illness at the beginning (T0) and after 3 months of an unchanged treatment (T3). FINDINGS/RESULTS: Baseline HAMD-17 scores in TRD were higher than in BD (P < 0.001), but TRD patients had a greater improvement at end point (P = 0.003). Antidepressants with AP generated greater reductions in HAMD-17 in TRD compared with BD (P = 0.02). Importantly, in BD patients, the addition of AD compared with other treatment strategies failed to improve the outcome. The limitations of this study include possibly unrepresentative subjects from tertiary care settings, incomplete matching of BD and TRD subjects, nonrandomized treatment with unmatched agents, doses, and times, unknown treatment adherence, and nonblinded retrospective outcome assessments. Nevertheless, the findings may reflect real-world interactions of clinically selected pharmacotherapies. IMPLICATIONS/CONCLUSIONS: Combination of augmentation strategies such as AD+AP and/or MS showed a better clinical improvement in patients with TRD compared with BD suggesting a limited evidence for AD potentiation in BD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".