Efficacy of Transcranial Direct Current Stimulation on Pain Intensity and Functionality in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to investigate whether transcranial direct current stimulation is superior to control groups or other interventions for pain relief and improving functionality in knee osteoarthritis patients. METHODS: PubMed, the Physiotherapy Evidence Database, the Cochrane Library, ProQuest, and Scopus databases were searched from inception to July 2022 to identify randomized clinical trials. The main outcomes were subjective perception of pain intensity measured either with the visual analog scale or with the numeric rating scale; and the functionality, assessed with the Western Ontario and McMaster Universities Osteoarthritis Index. As secondary outcomes, pressure pain threshold, conditioned pain modulation, and its safety were evaluated. RESULTS: We identified 10 randomized clinical trials (634 participants). The results showed an important effect favoring transcranial direct current stimulation for pain relief (mean difference = -1.1 cm, 95% confident interval = -2.1 to -0.2) and for improving functionality (standardized mean difference = -0.6, 95% confident interval = -1.02 to -0.26). There was also a significant improvement in pressure pain threshold (mean difference = 0.9 Kgf/cm 2 , 95% confident interval = 0.1 to 1.6). The certainty of evidence according to Grades of Recommendation Assessment, Development and Evaluation was generally moderate. CONCLUSIONS: Our findings suggest that transcranial direct current stimulation is a safe treatment for reducing pain intensity, improving functionality, and the pressure pain thresholds in patients with knee osteoarthritis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".