Acupuncture for the alleviation of lateral epicondyle pain: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Lateral epicondyle pain is a common complaint in North America. In the past 10 yr acupuncture has become increasingly recognized as an alternative treatment for pain, including epicondyle pain. This review evaluates the effectiveness of acupuncture as a treatment for lateral epicondylitis using the appropriate analysis. METHODS: Online bibliographic database searches in any language from Medline, PsychINFO, CINAHL, Healthstar, PMID, CAM, EMBASE, Cochrane Database of Systematic Review (3rd quarter 2003), articles listed in reference lists of key articles and the author's personal files were performed. Randomized and quasi-randomized controlled trials examining the effects of acupuncture on lateral epicondyle pain were selected. From the six studies that met inclusion criteria, the first author, year of publication, population studied, dropout rate, treatment plan, assessment scale and outcome measures were extracted. Study quality was determined by using the Jadad scale, in which all studies were rated as high quality. A best evidence synthesis approach was used to analyse the data presented in the six studies. RESULTS: All the studies suggested that acupuncture was effective in the short-term relief of lateral epicondyle pain. Five of six studies indicated that acupuncture treatment was more effective compared to a control treatment. CONCLUSIONS: There is strong evidence suggesting that acupuncture is effective in the short-term relief of lateral epicondyle pain.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".