Acupuncture for Depression: A Review of Clinical Applications
Bibliographic record
Abstract
While increasing numbers of patients are seeking acupuncture treatment for depression in recent years, there is limited evidence of the antidepressant (AD) effectiveness of acupuncture. Given the unsatisfactory response rates of many Food and Drug Administration-approved ADs, research on acupuncture remains of potential value. Therefore, we sought to review the efficacy and safety of acupuncture treatment for depression in clinical applications. We conducted a PubMed search for publications through 2011. We assessed the adequacy of each report and abstracted information on reported effectiveness or efficacy of acupuncture as monotherapy for major depressive disorder (MDD) and as augmentation of ADs. We also examined adverse events associated with acupuncture, and evidence for acupuncture as a means of reducing side effects of ADs. Published data suggest that acupuncture, including manual-, electrical-, and laser-based, is a generally beneficial, well-tolerated, and safe monotherapy for depression. However, acupuncture augmentation in AD partial responders and nonresponders is not as well studied as monotherapy; and available studies have only investigated MDD, but not other depressive spectrum disorders. Manual acupuncture reduced side effects of ADs in MDD. We found no data on depressive recurrence rates after recovery with acupuncture treatment. Acupuncture is a potential effective monotherapy for depression, and a safe, well-tolerated augmentation in AD partial responders and nonresponders. However, the body of evidence based on well-designed studies is limited, and further investigation is called for.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".