Safety of Acupuncture: Overview of Systematic Reviews
Bibliographic record
Abstract
Acupuncture is increasingly used worldwide. It is becoming more accepted by both patients and healthcare providers. However, the current understanding of its adverse events (AEs) is fragmented. We conducted this overview to collect all systematic reviews (SRs) on the AEs of acupuncture and related therapies. MEDLINE and EMBASE were searched from inception to December 2015. Methodological quality of included reviews was assessed with a validated instrument. Evidence was narratively reported. Seventeen SRs covering various types of acupuncture were included. Methodological quality of the reviews was overall mediocre. Four major categories of AEs were identified, which are organ or tissue injuries (13 reviews, median: 36 cases, median deaths: 4), infections (11 reviews, median: 17 cases, median deaths: 0.5), local AEs or reactions (12 reviews, median: 8.5 cases, no deaths were reported), and other complications such as dizziness or syncope (11 reviews, median: 21 cases, no deaths were reported). Minor and serious AEs can occur during the use of acupuncture and related modalities, contrary to the common impression that acupuncture is harmless. Serious AEs are rare, but need significant attention as mortality can be associated with them. Referrals should consider acupuncturists' training credibility, and patient safety should be a core part of acupuncture education.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".