Treatment of Resistant Depression by Adding Noradrenergic Agents to Lithium Augmentation of SSRIs
Bibliographic record
Abstract
OBJECTIVE: To examine the efficacy of second-line augmentation with noradrenergic antidepressants (NAs) in depressed patients who partially responded to lithium augmentation of selective serotonin-reuptake inhibitors (SSRIs). CASE SUMMARY: Six patients with major depression or double depression (major depression and dysthymia) who were partially responsive to lithium and SSRI treatment were given either bupropion or desipramine, in an open clinical manner. Improvement was determined and rated by a psychiatrist based on clinical judgment guided by the Clinical Global Impression (CGI) improvement scale and by the Global Assessment of Functioning (GAF) as described in Axis V in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. RESULTS: Among the 6 depressed patients with partial remission (much improved in symptoms and moderate functional improvement: CGI score 2, GAF score 51-60) while taking the SSRI and lithium combination, 2 showed complete remission (very much improved in symptoms and good functioning: CGI 1, GAF 80-100) and 3 achieved near-complete remission (very much improved in symptoms and significant functional recovery: CGI 1, GAF 61-80) when given either bupropion or desipramine. One patient did not show any additional clinical or functional improvement. Second-line augmentation with bupropion was better tolerated than desipramine. CONCLUSIONS: This clinical observation suggests that second-line augmentation with NAs may be a viable option to optimize recovery in depressed patients with a partial response to lithium augmentation of SSRIs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".