A network meta-analysis on the effectiveness and safety of acupuncture in treating patients with major depressive disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it