A network meta-analysis on the effectiveness and safety of acupuncture in treating patients with major depressive disorder
Bibliographic record
Abstract
Acupuncture is an important alternative therapy in treating major depressive disorder (MDD), but its efficacy and safety are still not well assessed. This study is the first network meta-analysis exploring the effectiveness and safety of acupuncture, common pharmacological treatments or other non-medication therapies for MDD. Eight databases including PubMed, Embase, Allied and Complementary Medicine Database, Cochrane Library, Wan Fang Data, China National Knowledge Infrastructure, China Biology Medicine disc, and Chongqing VIP Database were searched up to Jan 17, 2021. Articles were screened and selected by two reviewers independently. We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the certainty of the evidence. A total of 71 eligible studies were included. The network analysis results indicated that the combined interventions of electro-acupuncture (EA) with selective serotonin reuptake inhibitors (SSRIs) and manual acupuncture (MA) with SSRIs were more effective in improving depression symptoms compared with acupuncture alone, pharmacological interventions alone, or other inactive groups. Among all the regimens, EA with SSRIs was found to have the highest effect in improving depression symptoms of MDD. In addition, there were slight differences in the estimations of the various treatment durations. The combination of acupuncture and serotonin-norepinephrine reuptake inhibitors (SNRIs) was found to be more effective than SNRIs alone. In conclusion, acupuncture and its combinations could be safe and effective interventions for MDD patients. EA with SSRIs seems to be the most effective intervention among the assessed interventions. Well-designed and large-scale studies with long-term follow-up should be conducted in the future.
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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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.047 |
| 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 | 0.003 |
| 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".