EP120 Transcranial Direct Current Stimulation for Chronic Pain Management in Knee Osteoarthritis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background and Aims Knee osteoarthritis (KOA) is a prevalent degenerative disease characterized by pain and functional impairment. While traditional pain management provides limited relief, Transcranial Direct Current Stimulation (tDCS) has emerged as a potential modality for non-invasive pain modulation. We conducted a systematic review and meta-analysis evaluating the efficacy of active versus sham tDCS in these patients. Methods PubMed, EMBASE and Cochrane were searched for randomized controlled trials (RCTs) comparing active M1-SO tDCS to sham tDCS in patients diagnosed with KOA experiencing chronic pain. We assessed WOMAC (Western Ontario and McMaster Universities Osteoarthritis) index and pain score changes in different time points following treatment sessions. RevMan 5.4 and the RoB-2 tool were used for statistical analyses and risk of bias evaluation, respectively. Results We pooled 9RCTs including 476 patients, 50% undergoing active tDCS. The initial assessment, comparing treatment-end pain scores with baseline scores revealed a significantly favorable effect for tDCS (figure 1). Two additional measurements were conducted after the conclusion of the treatment. The first, performed after 3-5 weeks, revealed significantly reduced scores in the active tDCS group (figure 2). The second, conducted after 2-3 months, indicated no statistically significant differences (Mean Difference -0.65; 95%CI -1.35 to 0.05; p<0.07; I2=49%; 3RCTs; 278 patients). Regarding the WOMAC scores, active tDCS also exhibited a significant decrease in comparison to the control group (figure 3). Conclusions Our findings suggest that active tDCS holds promise as an adjunctive therapy to standard pain management of chronic pain in knee OA as it may decrease pain and increase function.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".