Citalopram versus other antidepressants for late‐life depression: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy and tolerability of citalopram when compared to other antidepressants for late-life depression (LLD). METHODS: We searched electronic databases and trial registries to identify randomized controlled trials comparing citalopram to other antidepressants for LLD. Study quality was assessed using the Cochrane collaboration risk of bias tool. We summarized the efficacy of citalopram compared to other antidepressants by examining rates of depression remission, depression response and change in depression symptom scores. Medication tolerability was assessed through trial withdrawals due adverse events and withdrawals due to any cause. We used meta-analysis to determine the odds ratios (OR) of efficacy and tolerability outcomes for citalopram compared to other antidepressants. RESULTS: Seven studies comparing citalopram (N = 647) to other antidepressants (N = 641) for LLD were identified including four studies with tricyclic comparators and three studies with non-tricyclic comparators. Most of the studies had methodological limitations that placed them at risk for potential bias. The majority of studies reported no significant differences between citalopram and comparator medications for depression efficacy or tolerability outcomes. Meta-analysis did not find any significant differences between citalopram and other antidepressants for depression remission [OR = 0.84; 95%CI: 0.56-1.28] or for trial withdrawals due to adverse effects [OR = 0.70; 95%CI: 0.48-1.02]. CONCLUSIONS: Currently there are few studies directly comparing citalopram to other antidepressants for LLD. The small number of studies and methodological issues in many studies limit any conclusions about the relative efficacy and tolerability of citalopram compared to other antidepressants. Well-designed studies comparing citalopram to other antidepressants for LLD are required.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".