Non-steroidal anti-inflammatory drugs and efficacy of antidepressants in major depressive disorder
Bibliographic record
Abstract
BACKGROUND: It has been proposed that non-steroidal anti-inflammatory drugs (NSAIDs) may interfere with the efficacy of antidepressants and contribute to treatment resistance in major depressive disorder (MDD). This effect requires replication and a test of whether it is specific to serotonin-reuptake inhibiting (SRI) antidepressants. METHOD: We tested the effect of concomitant medication with NSAIDs on the efficacy of escitalopram, a SRI antidepressant, and nortriptyline, a tricyclic antidepressant, among 811 subjects with MDD treated for up to 12 weeks in the GENDEP study. Effects of NSAIDs on improvement of depressive symptoms were tested in mixed-effect linear models. Effects on remission were tested in logistic regression. Age, sex, baseline severity and centre of recruitment were considered as potential confounding factors. RESULTS: Ten percent (n=78) of subjects were taking NSAIDs during the antidepressant treatment. Older subjects were significantly more likely to take NSAIDs. After controlling for age, sex, centre of recruitment and baseline severity, concomitant medication with NSAIDs did not significantly influence the efficacy of escitalopram [β=0.035, 95% confidence interval (CI) -0.145 to 0.215, p=0.704] or nortriptyline (β=0.075, 95% CI -0.131 to 0.281, p=0.476). Although slightly fewer subjects who took NSAIDs reached remission [odds ratio (OR) 0.80, 95% CI 0.49-1.31, p=0.383], this non-significant effect was reversed after controlling for age, sex, baseline severity and recruitment centre effects (OR 1.04, 95% CI 0.61-1.77, p=0.882). CONCLUSIONS: NSAIDs are unlikely to affect the efficacy of SRI or other antidepressants. Concurrent use of NSAIDs and antidepressants does not need to be avoided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".