Nicotinic Antagonist Augmentation of Selective Serotonin Reuptake Inhibitor-Refractory Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: There is evidence for nicotinic hypercholinergic mechanisms in depression. Clinical relationships between tobacco use and depression suggest important effects of nicotine in major depressive disorder (MDD). It has been hypothesized that cigarette smoking may exert antidepressant effects, presumably mediated through stimulation of nicotinic acetylcholine receptor systems. We compared the nicotinic antagonist, mecamylamine hydrochloride (MEC), with placebo as an augmentation strategy for patients with MDD who were refractory to selective serotonin reuptake inhibitor (SSRI) treatment. METHODS: Twenty-one SSRI-treated subjects with MDD were randomized to MEC (up to 10 mg/d; n = 11) or placebo (PLO group; n = 10) during an 8-week trial. The primary outcome measure was the change in depressive symptoms assessed using the 17-item Hamilton Depression Rating Scale during the 8-week trial. RESULTS: There was a significant reduction in 17-item Hamilton Depression Rating Scale scores in the MEC versus PLO groups, as evidenced by a significant medication x time interaction (F1,19 = 6.47, P < 0.05). Five (45.5%) of 11 subjects in the active study medication group demonstrated a 50% or more decrease in depressive symptoms from baseline as compared with 1 (10%) of 10 subjects assigned to placebo medication, but this difference was not significant (P = 0.15; Fisher exact test). The primary side effects of MEC were constipation and orthostatic hypotension. CONCLUSIONS: These preliminary findings suggest that the nicotinic acetylcholine receptor antagonist, MEC, may have utility as an augmentation strategy for patients with SSRI-refractory MDD.
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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 | 0.000 |
| 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 | 0.000 | 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".