Dry Needling: A Scoping Review of Adverse Outcomes
Bibliographic record
Abstract
Introduction:Dry needling (DN) is commonly used in physiotherapy to treat musculoskeletal pain. While some evidence suggests modest short-term relief, the overall efficacy remains inconsistent, and the safety profile is not well established. Adverse events are frequently underreported, and few reviews have systematically examined the range and quality of harm-related evidence. This scoping review aimed to identify and categorize reported adverse outcomes associated with DN and related needling therapies in adults with musculoskeletal conditions. We also evaluated how adverse events are tracked and reported, along with key methodological limitations in the literature.Materials and Methods: We conducted a comprehensive search of six databases (PubMed, MEDLINE, EMBASE, Scopus, Web of Science, and Google Scholar) for studies published between January 2000 and April 2025. Eligible studies included randomized controlled trials, observational studies, case reports, and systematic reviews reporting adverse events related to DN and intramuscular stimulation (IMS). Data were charted based on adverse event type, severity, and reporting quality. Results: Of 2,258 records screened, 26 studies met inclusion criteria. Adverse events ranged from minor issues (e.g., bruising, soreness) to serious complications including pneumothorax, deep infection, nerve injury, and spinal hematoma. Minor effects were reported in up to 50% of treatments. Underreporting was widespread, and most studies exhibited significant methodological flaws, such as small sample sizes, inadequate blinding, and publication bias.Conclusion: DN poses a nontrivial risk of harm. Rigorous safety monitoring, transparent reporting, and stronger study designs are urgently needed to guide responsible clinical use.
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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 | 0.040 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".