Global Benefit-risk Evaluation of Antidepressant Action: Comparison of Pooled Data for Venlafaxine, SSRIs, and Placebo
Bibliographic record
Abstract
Do antidepressants have an equivalent risk-benefit ratio? Venlafaxine, a serotonin-norepinephrine reuptake inhibitor, is an effective antidepressant for treating major depression. The results of some clinical studies have suggested that venlafaxine may have more potent efficacy in sustaining remission in patients with major depression. Comparative clinical studies, however, lack suitable power to discern treatment differences in safety and also lack a quantitative basis for comparing risk and benefit. A global benefit-risk analysis of pooled data from eight randomized, double-blind, clinical trials of the safety and efficacy of venlafaxine and selective serotonin reuptake inhibitors (SSRIs) was performed. By using the ratio measure of risk-benefit, patients treated with venlafaxine (n=851) for 6-8 weeks experienced a relative gain of 1.57 compared with SSRI-treated patients (n=743) and a relative gain of 2.27 compared with placebo-treated patients (n=439). Subgroup analyses showed a relative gain of 1.35 for venlafaxine-treated patients (n=538) compared with fluoxetine-treated patients (n=549) and a relative gain of 2.53 compared with placebo-treated patients (n=357). A dose-response relationship was apparent between low (<75 mg/day), medium (75-150 mg/day), and high (>150 mg/day) dosages of venlafaxine; r values were 0.758, 0.822, and 1.181, respectively (P=.023, high dosage versus placebo; P=.030, medium dosage versus placebo). Important differences in risk and benefit exist between venlafaxine and SSRIs as a group compared with fluoxetine alone. A significant gain in benefit-risk in the treatment of major depression was observed with an increase in venlafaxine dosage from 75->150 mg/day.
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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 | 0.064 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.033 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".