Efficacy and tolerability of antidepressants in individuals suffering from physical conditions and depressive disorders: network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antidepressants are effective for depression, but most evidence excludes individuals with comorbid physical conditions. AIMS: To assess antidepressants' efficacy and tolerability in individuals with depression and comorbid physical conditions. METHODS: Systematic review and network meta-analysis of randomised controlled trials (RCTs). Co-primary outcomes were efficacy on depressive symptoms and tolerability (participants dropping out because of adverse events). Bias was assessed with the Cochrane Risk-of-Bias 2 tool and certainty of estimates with the Confidence in Network Meta-Analysis approach. A study protocol was registered in advance (https://osf.io/9cjhe/). RESULTS: Of the 115 included RCTs, 104 contributed to efficacy (7714 participants) and 82 to tolerability (6083 participants). The mean age was 55.7 years and 51.9% of participants were female. Neurological and cardiocirculatory conditions were the most represented (26.1% and 18.3% of RCTs, respectively). The following antidepressants were more effective than placebo: imipramine, nortriptyline, amitriptyline, desipramine, sertraline, paroxetine, citalopram, fluoxetine, escitalopram, mianserin, mirtazapine and agomelatine, with standardised mean differences ranging from -1.01 (imipramine) to -0.34 (escitalopram). Sertraline and paroxetine were effective for the largest number of ICD-11 disease subgroups (four out of seven). In terms of tolerability, sertraline, imipramine and nortriptyline were less tolerated than placebo, with relative risks ranging from 1.47 (sertraline) to 3.41 (nortriptyline). For both outcomes, certainty of evidence was 'low' or 'very low' for most comparisons. CONCLUSION: Antidepressants are effective in individuals with comorbid physical conditions, although tolerability is a relevant concern. Selective serotonin reuptake inhibitors (SSRIs) have the best benefit-risk profile, making them suitable as first-line treatments, while tricyclics are highly effective but less tolerated than SSRIs and placebo.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.053 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".