High-intensity electroacupuncture is superior to low-intensity electroacupuncture for knee osteoarthritis: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background: Electroacupuncture (EA) has been demonstrated to be efficacious and safe in patients with knee osteoarthritis (KOA), yet the optimal current intensity for pain control in KOA remains unspecified. The present meta-analysis aimed to compare the effects of high-intensity and low-intensity EA in terms of pain relief and functional improvement in KOA. Methods: A thorough and comprehensive literature search for randomized controlled trials (RCTs), all looking at the intensity of EA for KOA, was carried out in PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science Journal Citation Report (VIP) and Wanfang database, as well as ClinicalTrials.gov. All databases were searched from their inception until April 2022. Study quality was assessed using the Cochrane risk of bias (RoB)2 tool. Finally, a meta-analysis of all eligible RCTs was performed using Review Manager 5.3. Results: Three studies with 472 individuals were included in the meta-analysis. The pain intensity reductions were significantly different between the high-intensity EA group and low-intensity EA group (mean difference (MD) = −0.22, 95% confidence interval (CI) = −0.26 to −0.18, p < 0.00001). There was no significant difference between the two groups in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores (MD = −3.62, 95% CI = −12.22 to 4.98, p = 0.41). High-intensity EA significantly improved emotional scale (ES) scores compared to low-intensity EA (MD = −0.72, 95% CI = −0.76 to −0.67, p < 0.00001). Conclusion: The findings of this systematic review and meta-analysis indicated that high-intensity EA provides superior pain relief and has a bigger impact on emotional scale scores in patients with KOA.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".