Is acupuncture effective in improving pain in patients with kneeosteoarthritis?
Bibliographic record
Abstract
Objective: The objective of this selective EBM review is to determine whether or not “Is acupuncture effective in improving pain in patients with knee osteoarthritis?” Study design: A systematic review of three randomized controlled trials (RCTs) published in between 2015-2018. Data sources: All three RCTs were discovered using PubMed. The articles were published in English in peer-reviewed journals and selected based on applicability to the clinical question. Outcome measured: Based on the studies done by Lin et al. and Chen et al., using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), a self-reported measure of lower extremity pain, stiffness, and function. In the RCT led by Helianthi et al., the Lequesne index was used, an interview format that includes pain, maximum distance walked, and activities of daily living. Higher scores indicate an increase in worse symptoms and function. Results: In the RCT led by Lin et al., there were no significant differences among the treatment groups between traditional acupuncture and sham acupuncture two weeks post intervention (p=.684). In the RCT led by Helianthi et al., laser acupuncture led to a decrease in pain and an increase in function by an improvement of the Lequesne Index compared to the control group (p<.0001), indicated by a mean change from baseline of 6.48. In the RCT led by Chen et al. both groups showed improvement from therapy with no significant differences between penetrating acupuncture and non-penetrating acupuncture in WOMAC response at (p=.148). Conclusion: Only the study led by Helianthi et a., demonstrated that acupuncture led to significantly reduced symptom severity as measured by the Lequesne index. This suggests that acupuncture may not be an effective and beneficial treatment for all patients for the treatment of knee osteoarthritis. Future studies should include a large sample size.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".