Comparative Efficacy and Safety of Selective Serotonin Reuptake Inhibitors and Serotonin‐Norepinephrine Reuptake Inhibitors in Older Adults: A Network Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: To establish the comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults using the network meta-analysis approach. DESIGN: Systematic review and network meta-analysis. PARTICIPANTS: Individuals aged 60 and older. MEASUREMENTS: Data on partial response (defined as at least 50% reduction in depression score from baseline) and safety (dizziness, vertigo, syncope, falls, loss of consciousness) were extracted. A Bayesian network meta-analysis was performed on the efficacy and safety outcomes, and relative risks (RRs) with 95% credible intervals (CrIs) were produced. RESULTS: Fifteen randomized controlled trials were eligible for inclusion in the analysis. Citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, fluoxetine, and sertraline were represented. Reporting on partial response and dizziness was sufficient to conduct a network meta-analysis. Reporting on other outcomes was sparse. For partial response, sertraline (RR=1.28), paroxetine (RR=1.48), and duloxetine (RR=1.62) were significantly better than placebo. The remaining interventions yielded RRs lower than 1.20. For dizziness, duloxetine (RR=3.18) and venlafaxine (RR=2.94) were statistically significantly worse than placebo. Compared with placebo, sertraline had the lowest RR for dizziness (1.14) and fluoxetine the second lowest (1.31). Citalopram, escitalopram, and paroxetine all had RRs between 1.4 and 1.7. CONCLUSION: There was clear evidence of the effectiveness of sertraline, paroxetine, and duloxetine. There also appears to be a hierarchy of safety associated with the different antidepressants, although there appears to be a dearth of reporting of safety outcomes.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".