Metareview on Short-Term Effectiveness and Safety of Antidepressants for Depression: An Evidence-Based Approach to Inform Clinical Practice
Bibliographic record
Abstract
OBJECTIVES: To examine the available scientific literature for answers to clinically relevant questions regarding the effectiveness and tolerability of antidepressant drugs (ADs) for the acute phase treatment of depression and to assess the degree to which the literature supports the findings. METHODS: We used several sources to identify primary reviews: MEDLINE (1955 to April 2006), EMBASE (1980 to April 2006), PsycINFO (1980 to April 2006), and the Cochrane Library 2006 Issue 1. Additional searches were also carried out on the following databases of the National Health Service Centre for Reviews and Dissemination: Abstracts of Reviews of Effects, Health Technology Assessment, and Turning Research into Practice. We also searched the National Institute of Health and Clinical Excellence guidance website. We carried out a metareview of selected high-quality systematic reviews of short-term pharmacologic interventions with ADs for major depression. To assess efficacy, we followed the hierarchy of evidence proposed by the Centre for Evidence Based Medicine (Oxford), including only reviews of randomized controlled trials. To assess tolerability, we also considered observational data when randomized evidence was not available. RESULTS: There was randomized evidence that ADs are efficacious in primary care settings and that there may be small, but clinically important, differences in efficacy between ADs. There was no good evidence that an AD combined with an antipsychotic is superior to AD monotherapy in cases of psychotic depression or that intravenous administration leads to more rapid response. There was evidence that monoamine oxidase inhibitors are superior to tricyclic antidepressants, but not to selective serotonin reuptake inhibitors (SSRIs), in treating atypical depression. There is some evidence of harm related to the use of SSRIs in pregnancy but not to their use when breastfeeding. There is evidence that SSRIs may increase suicidal thoughts, but not actual suicide, in early-phase therapy. CONCLUSIONS: We found a substantial body of evidence regarding the benefits and harms of ADs in the treatment of depressive disorder. Nonetheless, there remains considerable residual uncertainty. The evidence is inadequate for generally applicable recommendations; in most cases, the balance between risks and benefits will need to be considered for individual patients. Clinicians should also be guided by the recommendations and warnings issued by drug regulatory authorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".