The Role of Transcranial Direct Current Stimulation in Managing Pain and Enhancing Mobility in Knee Osteoarthritis: A Narrative Review
Bibliographic record
Abstract
Knee osteoarthritis (OA) is a common condition characterised by chronic pain and reduced mobility, particularly in the elderly population. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation technique, has proven to be a promising intervention in managing OA. This narrative review aims to synthesize existing evidence on the effectiveness of this intervention in patients with OA, with a primary focus on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale as a key outcome.A comprehensive literature search was conducted in PubMed and Scopus databases from 2017 to 2024 to identify studies that investigated various aspects of tDCS, including its effects on clinical and experimental pain, neurophysiological mechanisms, combination therapies, and feasibility in different settings, according to predefined eligibility criteria. The review summarises findings from randomised controlled trials and pilot studies. Evidence indicates that tDCS over the primary motor cortex with the cathode over the contralateral supraorbital area effectively reduces pain severity, enhances pain modulation mechanisms, and improves mobility in knee OA patients. The findings highlight that tDCS improves functional outcomes as measured by WOMAC and benefits in clinical and experimental pain modulation. Preliminary findings are promising, necessitating large-scale trials to optimise protocols, address inconsistencies, and assess long-term effects. This review underscores the clinical relevance of using tDCS in managing OA and identifies gaps for future research, in long-term efficacy and standardised protocols.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".