Effectiveness of transcranial direct current stimulation on pain and function in knee osteoarthritis: a systematic review with meta-analysis based on PRISMA guidelines
Bibliographic record
Abstract
Introduction To evaluate the effectiveness of transcranial direct current stimulation (tDCS) on pain and quality of life in patients with knee osteoarthritis (OA). Methods The meta-analysis data source were PubMed (MEDLINE) and Cochrane (Central). Randomized controlled trials comparing active tDCS (in combination with other interventions or alone in knee OA patients) with sham tDCS published in English till July 2019 were analysed. The outcome measures were pain intensity (visual analogue scale and numeric rating scale) and quality of life (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]). Mean difference with 95% confidence interval for the active tDCS and sham groups were investigated. Results Four articles with 171 patients were included in the qualitative systematic review and 2 articles with 55 patients were included in the quantitative meta-analysis. The results revealed a statistically significant reduction in pain (visual analogue scale) in the active tDCS group compared with the sham group (Z = 7.32, p < 0.00001) and a significant improvement in WOMAC scores (Z = 2.31, p = 0.02), with high heterogeneity of 83%. Conclusions There is a significant improvement in pain and function in patients with knee OA after the application of active tDCS either alone or in combination. However, more studies are required to confirm the effectiveness of tDCS in knee OA. Owing to the promising results of tDCS in various pain conditions and in knee OA, it can be seen as a future tool for managing pain in the field of physiotherapy.
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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 | 0.047 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".