Remission, dropouts, and adverse drug reaction rates in major depressive disorder: a meta-analysis of head-to-head trials
Bibliographic record
Abstract
OBJECTIVE: To summarize remission rates and dropouts due to adverse drug reactions (ADRs) or lack of efficacy (LoE) of serotonin-norepinephrine reuptake inhibitors (SNRIs), selective serotonin-reuptake inhibitors (SSRIs), and tricyclic antidepressants (TCAs) in treating major depressive disorder. METHODS: We searched MEDLINE, EMBASE, IPA, and the Cochrane International Library from 1980-2005. Meta-analysis summarized outcomes from head-to-head randomized clinical trials comparing >or= 2 drugs from three antidepressants classes (SNRIs, and/or SSRIs, and/or TCAs) followed by >or= 6 weeks of treatment. Remission was a final Hamilton Depression Rating Scale (HAMD) score 0.05 for SNRIs versus TCAs; p < 0.001 for TCAs versus SSRIs and SNRIs versus SSRIs). When categorized as inpatients (n = 582) and outpatients (n = 1613), SNRIs had the highest remission rates (52.0% for 144 inpatients and 49.3% for 559 outpatients). SNRIs had lowest overall dropouts (26.1%), followed by SSRIs (28.4%), and TCAs (35.7%). Dropouts due to ADRs and LoE were 10.3% and 6.2% for SNRIs, 8.3% and 7.2% for SSRIs, and 19.8% and 9.9% for TCAs, respectively (p > 0.05 for ADR dropouts only). One limitation was the inclusion of only venlafaxine-XR; results may not be the same for immediate release forms. In addition, few studies reported remission rates. CONCLUSIONS: SNRIs had the highest efficacy remission rates (statistically significant for inpatients and outpatients), and the lowest overall dropout rates, suggesting clinical superiority in treating major depression.
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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 | 0.040 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.061 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".