Effectiveness of Transcranial Direct Current Stimulation and Pharmacotherapy in Pain Management in Patients with Chronic Pain
Bibliographic record
Abstract
Background: A variety of pharmacological and nonpharmacological methods are used to treat chronic pain. Transcranial direct current stimulation (tDCS) through stimulating the central and peripheral nerves is a different and promising method for the treatment of chronic pain. Objectives: The present study aimed to investigate the effectiveness of tDCS and pharmacotherapy in pain management in patients with chronic pain in Tehran, Iran. Methods: The present study followed a clinical trial design. The statistical population comprised all patients with chronic pain who were referred to Pardis Multidisciplinary Pain Clinic in Tehran within 2020 - 21. A total of 60 patients willing to participate in the study were selected using convenience sampling. The participants were randomly divided into three groups, including pharmacotherapy (treatment by gabapentin with a dosage of 600 mg twice per day), tDCS, and control (n = 20 per group). The research instrument included the McGill Pain Questionnaire. The data were analyzed using repeated-measures analysis of variance with SPSS software (version 24.0). Results: The results showed that both pharmacotherapy and tDCS interventions led to a reduction in the mean scores of pain management components, compared to the control group (P < 0.001). Furthermore, there was no significant difference between the effects of the two experimental groups on pain management components. Conclusions: The tDCS and pharmacotherapy were both shown to be effective in pain management in patients with chronic pain. Therefore, in addition to pharmacotherapy, tDCS is also recommended for the treatment of chronic pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".