Evaluation of the Separate and Combined Effects of Anodal tDCS over the M1 and F3 Regions on Pain Relief in Patients with Type-2 Diabetes Suffering from Neuropathic Pain
Bibliographic record
Abstract
Abstract Neuropathic pain (NP) is a common complication of chronic diabetes that negatively affects the routine functioning and sleep of patients. The present study aimed to investigate the separate and combined effects of anodal transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) and left dorsolateral prefrontal cortex (F3) regions on pain relief in patients with type-2 diabetes suffering from NP. The statistical population of this double-blind randomized clinical trial consisted of all the members of the Bonab Diabetes Association in 2022 aged 45 to 65 years who were diagnosed with NP by a specialist. A total of 48 patients who met the inclusion criteria were selected as the sample through purposive sampling. The participants were then randomly assigned into 4 groups, each attending 12 sessions of a special intervention (three times a week). The Short Form-McGill Pain Questionnaire-2 (SF-MPQ-2) was used for data collection. Data were statistically analyzed using SPANOVA, analysis of covariance, and the Bonferroni test. The results showed that perceived pain intensity significantly changed in all groups over time (F=11.48, P<0.001). The mean perceived pain intensity in the posttest was significantly lower in the M1 stimulation group than in the F3 stimulation group. Nevertheless, there was no significant difference between the two groups in terms of perceived pain intensity in the one-month and two-month follow-up stages. Considering its therapeutic potential for pain relief, the application of tDCS (over both M1 and F3) can be expanded for diabetic patients.
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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 | 0.001 |
| 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) | 0.002 | 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".