Antidepressants for treatment of depression in primary care: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION Evidence for the effectiveness of drug treatment for depression in primary care settings remains limited, with little information on newer antidepressant classes. AIM To update an earlier Cochrane review on the effectiveness of antidepressants in primary care to include newer antidepressant classes, and to examine the efficacy of individual agents. METHODS Selection criteria included antidepressant studies with a randomly assigned placebo group where half or more subjects were recruited from primary care. The Cochrane Collaboration Depression, Anxiety and Neurosis (CCDAN) group searched multiple databases to identify eligible studies. Data extraction was performed independently by two reviewers. Data were analysed using Revman version 5.3.5. RESULTS In total, 17 papers and 22 comparisons were included for analysis. Significant benefits in terms of response were found for tricyclic antidepressants (TCA) with a relative risk (RR) = 1.23 (95% CI, 1.01-1.48), and serotonin selective reuptake inhibitors (SSRI) with a RR = 1.33 (95% CI, 1.20-1.48). Mianserin was effective for continuous outcomes. Numbers needed to treat (NNT) for TCA = 8.5; SSRI = 6.5; and venlafaxine = 6. Most studies were industry-funded and of a brief duration (≤ 8 weeks). There was evidence of publication bias. There were no studies comparing newer antidepressants against placebo. CONCLUSION Antidepressants such as TCA, SSRI, SNRI (serotonin-norepinephrine reuptake inhibitor) and NaSSA (noradrenergic and specific serotonergic antidepressant) classes appear to be effective in primary care when compared with placebo. However, in view of the potential for publication bias and that only four studies were not funded by industry, caution is needed when considering their use in primary care.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".